[tab] /p/project1/envcomp/mihir/logs/train/qwen3-0.6b-sft-torch-crowd-code/qwen_8b_no_lora_bs_32_13034357.log (log)
#!/usr/bin/env bash

#SBATCH --nodes=64
#SBATCH --ntasks-per-node=1
#SBATCH --time=24:00:00
#SBATCH --cpus-per-task=5
#SBATCH --gres=gpu:4
#SBATCH --mem=200GB
#SBATCH --account=envcomp
#SBATCH --nodelist=jwb[0700-0799]
#SBATCH --signal=USR1@60
#SBATCH --output=/p/project1/envcomp/mihir/logs/train/qwen3-0.6b-sft-torch-crowd-code/%x_%j.log
#SBATCH --error=/p/project1/envcomp/mihir/logs/train/qwen3-0.6b-sft-torch-crowd-code/%x_%j.log
#SBATCH --job-name=qwen_8b_no_lora_bs_32


trap 'echo "Job killed by signal"' USR1

cat $0

module load CUDA/12
module load cuDNN/9.5.0.50-CUDA-12
module load NCCL/default-CUDA-12
module load Clang/18.1.8

source .venv/bin/activate

srun sh slurm/jobs/mihir/juelich/crowd-pilot/1-wave-anta-santa/qwen_8b_no_lora_bs_32_ctx_parallel.shHAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
HAS_NVLINK: 0 (detected 0 NVLink references)
--use-wandb --wandb-project crowd-pilot-miles --wandb-team instant-uv --wandb-group qwen8B_no_lora_bs_32_13034357 --wandb-run-id 13034357 --wandb-dir /p/scratch/envcomp/logs/ANTA_CLAUS
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] 
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.385000 1834365 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] 
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.385000 2039335 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] 
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.385000 3358348 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] 
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.385000 1871253 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] 
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.385000 2969284 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] 
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 1884110 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] 
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 2298516 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] 
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 1086274 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 4158530 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 4108767 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] 
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 3875004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] 
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 990580 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] 
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 1840514 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 4061004 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] 
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 2748290 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] 
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 4016919 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] 
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.386000 2552375 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 2368347 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] 
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 842944 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] 
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 132179 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1852400 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1005532 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 2095552 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1095854 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 3080522 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] 
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 707844 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 3137823 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 2323410 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1269189 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 3620889 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] 
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 91366 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1548729 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 2386424 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1557718 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] 
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 4031052 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 3783091 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] 
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 1178019 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] 
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 899066 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] 
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 345543 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] 
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 2849074 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] 
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 3612398 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] 
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.387000 918553 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] 
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.388000 1757992 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.541379593 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.527260014 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.586277454 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.582444648 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.571390347 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.559538858 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.563675351 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.630557535 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.528052271 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.551887205 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.579562425 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.522469639 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.586596965 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.552159159 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.551018915 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.493031740 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.591701163 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.530094206 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.565537583 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.587545704 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.511421638 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.542838593 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.554129777 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.521274648 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.504918077 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.552602076 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.636914066 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.493396967 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.559351518 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.566958114 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.602183519 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.539473580 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.602500735 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.578982338 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.521941991 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.551296087 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.555244670 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.579882601 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.474139360 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.577089905 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.603936282 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.607322181 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] 
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.394000 1432841 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] 
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.396000 2628166 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] 
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.397000 3540252 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] 
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.397000 2927834 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] 
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.397000 797134 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.635862783 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] 
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.400000 1554814 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.608740631 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.580115231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.553827512 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.630624359 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.565226863 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] 
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.418000 1833893 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] 
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.421000 2561980 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] 
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.424000 974116 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.583009494 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] 
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.425000 2410687 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.580310799 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] 
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.427000 4157487 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.581389857 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.559651052 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.563428384 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] 
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.436000 2767470 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] 
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.439000 3870112 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.549147387 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.629703578 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.632870242 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] 
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.459000 1490116 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] 
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.459000 646070 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] 
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.460000 1459219 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.572513589 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.652650896 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.657995004 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] 
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.485000 1041813 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.713181214 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] 
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.492000 99787 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] 
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.496000 2478330 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.669494269 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:34:54.675272944 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] 
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.551000 2540709 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.652256814 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] 
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] *****************************************
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. 
W1229 11:34:54.667000 3797500 torch/distributed/run.py:774] *****************************************
[W1229 11:34:54.848902604 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.
  import pynvml  # type: ignore[import]
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1538 - n_samples_per_prompt is set to 1, grpo_std_normalization will be set to False.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[2025-12-29 11:35:23] arguments.py:1583 - args.eval_max_context_len is not set. Use args.rollout_max_context_len 4100 as default value.
[W1229 11:35:24.484760444 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.485805703 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.489251192 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.459992538 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.489942888 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.460937653 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492088956 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.492731191 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.464859118 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.465588133 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522249987 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.523162015 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.468057438 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.468808373 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.498549100 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.451139770 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525375784 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.499406467 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.451971335 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.539019356 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526103771 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.500251683 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539886275 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.554018360 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554258851 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554840732 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.500944238 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.554995003 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.529571821 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530274123 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533859667 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530525984 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530805405 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530860186 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530541570 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534791237 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.455949998 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531524088 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524114303 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.456584091 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.456610331 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531927390 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.457330155 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525140933 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.533045885 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533380046 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.555856448 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.475174321 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533835030 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.534203573 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.459268397 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.476405636 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556757825 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534568887 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.476045065 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.459791070 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534941840 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.477461011 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.535353654 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554668231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.521633957 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.536141871 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.555208846 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.558775240 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.555926632 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.478202026 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.509199898 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556201845 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522977094 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.536048197 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.485792333 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.486036125 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559991190 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.510282377 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.479447422 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.502307801 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.537124107 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.481229491 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.487042333 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.524320871 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.487145333 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.446420436 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.595746331 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488083405 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.561561952 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543208041 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488526378 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.551501745 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.503828455 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525565387 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.447837419 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.482782810 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.552034480 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.490636184 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.453695613 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530286379 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.489512475 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544393833 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.562847591 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.489722116 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.491053839 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.544615405 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.552709667 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.460355180 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.460361440 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.597326137 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.597639132 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.460666223 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.454761569 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.589791813 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531664718 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.455097041 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492066699 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.553651866 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.598729900 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545720566 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492256181 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490436119 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.461587772 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.504924222 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.461777613 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.554196501 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.462154737 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.491599938 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.456084876 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506211242 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.436233759 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.591351264 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506233851 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.555178931 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506343262 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542437713 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.491867859 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.516252870 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.492910547 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.437545324 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.507322109 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.534201093 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548081179 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.507749082 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.543755828 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517261423 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.508009843 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.508269755 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541034732 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495200423 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495203783 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541113413 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571319756 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.535032228 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549054849 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517945396 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.453479048 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.496298525 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.509140231 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.509412652 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525173178 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.496347186 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.572450899 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517122226 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.517136236 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.494726361 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.542460842 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542463752 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.519145920 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.510518714 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.454564578 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.595246231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488845394 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.488844464 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526586154 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.495885520 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.511414332 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.546577682 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.518441578 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.518444058 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.489107333 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.511712795 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544046652 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.522157693 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.522314574 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.555194850 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.479977818 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.596795961 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490337409 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490339409 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.574964316 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.528188940 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.480494013 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548104150 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.513024166 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.490667008 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.548007121 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.528905463 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571760675 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.545967235 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.598206331 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539872039 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524271596 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.524276506 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.572187825 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.529766227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.576916091 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.482253309 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.482258259 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.557546669 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525356988 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525358618 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.464014159 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.599521700 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539167595 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550203560 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530779251 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.573597264 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.459353670 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524315863 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.544669373 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524415894 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548159409 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.522807388 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526857873 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.465530970 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.551290498 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.526890733 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.600822719 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542538625 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.574706584 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.589902482 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.484310647 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495673655 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.466326924 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.523905536 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.495787025 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.575210493 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.533401224 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.502193677 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.536594219 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.576711285 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.546678523 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.588834770 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.576452803 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.447772871 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.467797834 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527257038 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.529275537 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.529280727 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.462510858 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542872135 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.612140538 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.497321116 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.577491554 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.577346312 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.529994275 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527779359 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527798699 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530036995 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.548100607 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.518681138 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.603644008 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.551551282 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.448792873 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.497523057 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543456849 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.526371015 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.468848084 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.518946650 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.545785735 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.607122131 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530439924 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.578454552 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.527315400 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527378990 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.555595309 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.488027510 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.469785753 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.505474868 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.579527231 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527406443 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.470007365 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.543344054 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.499426312 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.499427702 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.593981471 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.488368153 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.537103893 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543758586 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.563828159 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.521766718 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.476925000 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550189227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.580549396 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.592466887 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.540620645 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.537924951 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564579586 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.564670136 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.592602358 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.451457073 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.532440947 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.466246501 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541640110 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.541677230 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531398502 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.452123061 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.452145091 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.546751736 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545573466 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.500917423 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.472042574 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.615740567 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.581074291 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522202940 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.610109295 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.582020815 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.558315332 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.501240535 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556211734 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.545008955 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.500663716 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530383845 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.533862175 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.501682517 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.522682756 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549529099 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.552308218 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556627737 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.610932992 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.530617968 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.546780012 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545731195 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.501364280 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559288955 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559297595 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.524316116 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525926453 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.583415242 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.547202944 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.526306236 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.579442938 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.540770011 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.567373148 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.492080456 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.525077368 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.531671496 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.579890472 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527788647 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.542428918 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.525920772 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.614018808 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.535771027 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.562673368 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.562705948 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571153078 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.571384750 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.620642137 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.543046471 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.599034247 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.527494302 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550924754 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531943309 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.530860967 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531495899 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.570837181 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.548297870 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.603255810 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.539235683 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.531403702 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.572376093 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.510575335 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.606723300 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.606931451 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.556802551 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.535608793 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.581122621 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.516298686 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.516298716 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559412251 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.559679533 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.468184334 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.494393167 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.582597989 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.482755317 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.581775453 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.581802404 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.609758439 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549157600 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.559250149 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.612248426 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.557917364 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.557952195 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.560244060 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.560383041 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.562754453 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.575171532 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.538997729 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.598953188 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.627240237 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.518151765 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.517413119 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.561831150 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.573356840 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.539532623 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.563638638 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.564973794 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.565183916 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.574644369 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.574735780 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.490958240 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.491041891 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.560831476 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.519404199 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.570756227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.601062049 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.597117792 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.597134932 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.575135672 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.563787855 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542121144 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.548951168 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.557465771 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.550806978 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.558448046 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.613075784 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.613077304 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.629415855 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.541555227 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.635147743 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.549101919 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542694486 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542695406 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.542719156 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577618430 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577622050 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577627030 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.577628370 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.558612318 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.520026675 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.521174212 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.521174322 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.615677962 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.615679802 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.541449717 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.541453897 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544244963 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544248483 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544252323 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544253203 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564095908 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564098468 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564105478 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.586221358 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.586227378 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.586260292 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.582042148 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.582043728 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.563222711 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.587167673 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.587198794 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.587336737 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.565371128 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.617142669 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564526595 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.631417501 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.588358265 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.569668763 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.568489713 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.569708693 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.578730057 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.495034138 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.495050388 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.604778760 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.578777637 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.564791112 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.544935873 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.566369445 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.545074694 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.566504166 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.567807950 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.591876600 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.503579194 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.588186615 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.594328423 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.637794644 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.506345673 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.596387984 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.596419844 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.594055651 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550735794 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.550755995 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.601022639 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.596908273 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.512652924 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.512655474 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.512780204 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554120617 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.554140727 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.558293656 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.558322477 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.602616397 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.520039451 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.560851931 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.520722096 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.560953282 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.528598047 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.589841787 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.591241079 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.594192424 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.598314959 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.598320939 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.599103855 socket.cpp:755] [c10d] The client socket cannot be initialized to connect to [jwb0702.juwels]:29500 (errno: 97 - Address family not supported by protocol).
[W1229 11:35:24.603339401 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[W1229 11:35:24.609106750 Utils.hpp:137] Warning: Environment variable NCCL_ASYNC_ERROR_HANDLING is deprecated; use TORCH_NCCL_ASYNC_ERROR_HANDLING instead (function operator())
[Gloo] Rank 0 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 0] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 1 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 1] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 3 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 3] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 2 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 2] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 0] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=0
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 0] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 3] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 2] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=2
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 3] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 1] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=1
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 2] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 1] CP initialized via device mesh
[2025-12-29 11:35:26] wandb_utils.py:72 - W&B logs will be stored in: /p/scratch/envcomp/logs/ANTA_CLAUS
[Gloo] Rank 4 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 6 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 5 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 6] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 4] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 5] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 7 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 7] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 7] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=7
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 6] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=6
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 5] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=5
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 4] Device mesh: dp_size=32, cp_size=8, dp_rank=0, cp_rank=4
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 7] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 4] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 6] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 5] CP initialized via device mesh
[Gloo] Rank 10 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 10] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 8 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 9 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 8] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 9] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 11 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 11] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 9] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=1
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 8] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=0
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 10] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=2
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 8] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 9] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 10] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 11] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=3
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 11] CP initialized via device mesh
[Gloo] Rank 13 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 13] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 15 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 12 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 15] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 12] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 14 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 14] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 13] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=5
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 15] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=7
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 12] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=4
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 13] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 15] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 12] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 14] Device mesh: dp_size=32, cp_size=8, dp_rank=1, cp_rank=6
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 14] CP initialized via device mesh
[Gloo] Rank 16 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 18 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 16] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 18] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 19 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 19] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 17 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:26] train_sft.py:120 - [Rank 17] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 19] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=3
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 16] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=0
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 18] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=2
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 18] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 19] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 16] CP initialized via device mesh
[2025-12-29 11:35:26] train_sft.py:146 - [Rank 17] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=1
[2025-12-29 11:35:26] train_sft.py:154 - [Rank 17] CP initialized via device mesh
[Gloo] Rank 21 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 21] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 22 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 20 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 23 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 22] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 20] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 23] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 23] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 20] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 21] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 22] Device mesh: dp_size=32, cp_size=8, dp_rank=2, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 23] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 21] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 20] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 22] CP initialized via device mesh
[Gloo] Rank 24 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 24] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 27 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 27] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 26 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 25 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 26] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 25] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 27] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 27] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 26] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 25] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 26] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 25] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 24] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 24] CP initialized via device mesh
[Gloo] Rank 28 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 28] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 29 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 30 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 29] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 30] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 28] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 29] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 28] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 30] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 29] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 30] CP initialized via device mesh
[Gloo] Rank 31 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 31] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 31] Device mesh: dp_size=32, cp_size=8, dp_rank=3, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 31] CP initialized via device mesh
[Gloo] Rank 32 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 32] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 34 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 33 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 34] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 33] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 35 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 35] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 32] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 34] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 33] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 32] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 34] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 33] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 35] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 35] CP initialized via device mesh
[Gloo] Rank 37 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 36 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 37] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 39 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 38 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 36] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 39] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 38] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 38] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 37] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 36] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 39] Device mesh: dp_size=32, cp_size=8, dp_rank=4, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 37] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 38] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 36] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 39] CP initialized via device mesh
[Gloo] Rank 41 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 40 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 41] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 40] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 43 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 42 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 43] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 42] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 42] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 40] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 43] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 41] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 42] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 43] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 40] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 41] CP initialized via device mesh
[Gloo] Rank 44 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 47 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 45 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 44] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 45] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 47] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 46 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 46] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 46] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 44] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 45] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 47] Device mesh: dp_size=32, cp_size=8, dp_rank=5, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 46] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 44] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 45] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 47] CP initialized via device mesh
[Gloo] Rank 49 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 49] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 48 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 50 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 50] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 48] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 49] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=1
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 50] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 48] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=0
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 49] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 50] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 48] CP initialized via device mesh
[Gloo] Rank 51 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 51] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 51] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=3
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 51] CP initialized via device mesh
wandb: Tracking run with wandb version 0.23.1
wandb: W&B syncing is set to `offline` in this directory. Run `wandb online` or set WANDB_MODE=online to enable cloud syncing.
wandb: Run data is saved locally in /p/scratch/envcomp/logs/ANTA_CLAUS/wandb/offline-run-20251229_113526-fb070njd
[Gloo] Rank 53 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 55 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 52 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 53] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 54 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 55] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 52] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:27] train_sft.py:120 - [Rank 54] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 52] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=4
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 54] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=6
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 52] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 54] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 55] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=7
[2025-12-29 11:35:27] train_sft.py:146 - [Rank 53] Device mesh: dp_size=32, cp_size=8, dp_rank=6, cp_rank=5
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 55] CP initialized via device mesh
[2025-12-29 11:35:27] train_sft.py:154 - [Rank 53] CP initialized via device mesh
[Gloo] Rank 56 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 58 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 56] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 58] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 57 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 59 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 59] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 57] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 56] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 58] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 57] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 59] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 56] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 58] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 59] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 57] CP initialized via device mesh
[Gloo] Rank 61 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 61] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 63 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 62 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 63] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 62] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 60 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 60] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 63] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 60] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 61] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 62] Device mesh: dp_size=32, cp_size=8, dp_rank=7, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 60] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 63] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 61] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 62] CP initialized via device mesh
[Gloo] Rank 64 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 66 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 64] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 66] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 67 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 67] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 65 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 65] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 64] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 67] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 66] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 65] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 67] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 64] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 66] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 65] CP initialized via device mesh
[Gloo] Rank 68 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 68] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 70 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 69 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 71 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 70] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 69] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 71] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 70] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 69] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 71] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 70] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 69] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 68] Device mesh: dp_size=32, cp_size=8, dp_rank=8, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 71] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 68] CP initialized via device mesh
[Gloo] Rank 72 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 73 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 74 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 72] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 73] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 74] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 75 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 75] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 74] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 72] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 73] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 73] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 72] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 74] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 75] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 75] CP initialized via device mesh
[Gloo] Rank 76 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 76] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank [Gloo] Rank 79 is connected to 25578 peer ranks.  is connected to Expected number of connected peer ranks is : 255255 peer ranks. 
Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 79] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 78] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 77 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 77] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 79] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 78] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 76] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 79] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 78] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 76] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 77] Device mesh: dp_size=32, cp_size=8, dp_rank=9, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 77] CP initialized via device mesh
[Gloo] Rank 80 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 81 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 80] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 82 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 83 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 81] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 83] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 82] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 82] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 81] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 82] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 81] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 80] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 83] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 80] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 83] CP initialized via device mesh
[Gloo] Rank 84 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 85 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 86 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 84] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 87 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 85] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 86] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 87] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 85] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 87] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 84] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 86] Device mesh: dp_size=32, cp_size=8, dp_rank=10, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 85] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 87] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 84] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 86] CP initialized via device mesh
[Gloo] Rank 88 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 89 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 88] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 90 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 89] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 90] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 91 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 91] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 88] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=0
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 89] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 90] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=2
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 91] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=3
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 88] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 89] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 90] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 91] CP initialized via device mesh
[Gloo] Rank 92 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 92] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 94 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 95 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 93 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 95] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 94] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:28] train_sft.py:120 - [Rank 93] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 94] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=6
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 93] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=5
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 92] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=4
[2025-12-29 11:35:28] train_sft.py:146 - [Rank 95] Device mesh: dp_size=32, cp_size=8, dp_rank=11, cp_rank=7
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 94] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 93] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 95] CP initialized via device mesh
[2025-12-29 11:35:28] train_sft.py:154 - [Rank 92] CP initialized via device mesh
[Gloo] Rank 96 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 97 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 98 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 96] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 99 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 97] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 98] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 99] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 98] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 99] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 97] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 99] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 98] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 97] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 96] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 96] CP initialized via device mesh
[Gloo] Rank 100 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 101 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 103 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 102 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 100] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 101] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 103] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 102] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 103] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 100] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 102] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 101] Device mesh: dp_size=32, cp_size=8, dp_rank=12, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 103] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 100] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 102] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 101] CP initialized via device mesh
[Gloo] Rank 104 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 105 is connected to 255[Gloo] Rank  peer ranks. Expected number of connected peer ranks is : 106 is connected to 255255 peer ranks. Expected number of connected peer ranks is : 
255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 104] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 107 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 106] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 105] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 107] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 104] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 107] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 105] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 106] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 104] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 107] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 106] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 105] CP initialized via device mesh
[Gloo] Rank 108 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 109 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 110 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 108] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 111 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 109] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 110] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 111] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 111] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 110] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 108] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 109] Device mesh: dp_size=32, cp_size=8, dp_rank=13, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 108] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 111] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 110] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 109] CP initialized via device mesh
[Gloo] Rank 112 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 113 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 114 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 115 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 112] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 113] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 114] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 115] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 112] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 114] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 113] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 115] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 113] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 112] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 114] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 115] CP initialized via device mesh
[Gloo] Rank 116 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 118 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 119 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 116] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 118] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 119] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 117 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 117] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 119] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 118] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 116] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 119] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 118] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 116] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 117] Device mesh: dp_size=32, cp_size=8, dp_rank=14, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 117] CP initialized via device mesh
[Gloo] Rank 120 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 121 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 122 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 120] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 123 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 121] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 122] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 123] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 123] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 120] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 121] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 122] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 120] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 122] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 121] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 123] CP initialized via device mesh
[Gloo] Rank 124 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 125 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 126 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 127 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 124] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 125] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 126] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 127] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 125] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 126] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 124] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 125] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 126] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 124] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 127] Device mesh: dp_size=32, cp_size=8, dp_rank=15, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 127] CP initialized via device mesh
[Gloo] Rank 128 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 129 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 130 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 131 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 129] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 128] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 130] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 131] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 131] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 129] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 129] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 131] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 128] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 130] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 128] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 130] CP initialized via device mesh
[Gloo] Rank 132 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 133 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 134 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 135 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 132] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 133] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 134] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 135] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 133] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 134] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 132] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 135] Device mesh: dp_size=32, cp_size=8, dp_rank=16, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 133] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 134] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 132] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 135] CP initialized via device mesh
[Gloo] Rank 136 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 137 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 136] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 138 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 139 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 137] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 138] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 139] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 138] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 139] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 136] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 137] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 138] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 136] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 139] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 137] CP initialized via device mesh
[Gloo] Rank 140 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 141 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 142 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 140] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 141] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 142] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 143 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 143] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 141] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 143] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 142] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 140] Device mesh: dp_size=32, cp_size=8, dp_rank=17, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 143] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 142] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 140] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 141] CP initialized via device mesh
[Gloo] Rank 144 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 145 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 144] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 146 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 147 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 145] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 146] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 147] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 144] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 147] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 145] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 146] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 144] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 147] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 145] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 146] CP initialized via device mesh
[Gloo] Rank 148 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 149 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 150 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 148] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 150] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 149] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 151 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 151] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 148] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 150] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 149] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 148] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 150] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 149] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 151] Device mesh: dp_size=32, cp_size=8, dp_rank=18, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 151] CP initialized via device mesh
[Gloo] Rank 152 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 153 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 152] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 153] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 155 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 154 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 155] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 154] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 152] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 153] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 154] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 155] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 152] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 153] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 154] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 155] CP initialized via device mesh
[Gloo] Rank 156 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 157 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 156] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 159 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 157] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 159] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 158 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 158] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 159] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 156] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 157] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 158] Device mesh: dp_size=32, cp_size=8, dp_rank=19, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 157] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 159] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 156] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 158] CP initialized via device mesh
[Gloo] Rank 160 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 161 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 160] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 161] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 163 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 162 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 163] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 162] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 161] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 163] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 162] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 161] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 163] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 162] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 160] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 160] CP initialized via device mesh
[Gloo] Rank 164 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 165 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 167 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 166 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 164] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 165] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 167] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 166] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 164] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 167] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 166] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 165] Device mesh: dp_size=32, cp_size=8, dp_rank=20, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 164] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 167] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 166] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 165] CP initialized via device mesh
[Gloo] Rank 168 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 168] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 169 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 170 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 171 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 169] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 170] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 171] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 168] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 171] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 169] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 170] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 169] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 168] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 171] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 170] CP initialized via device mesh
[Gloo] Rank 172 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 173 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 174 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 175 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 173] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 172] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 174] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 175] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 173] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 174] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 172] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 175] Device mesh: dp_size=32, cp_size=8, dp_rank=21, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 173] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 174] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 172] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 175] CP initialized via device mesh
[Gloo] Rank 176 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 177 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 178 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 176] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 179 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 178] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 180 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 181 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 177] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 179] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 182 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 183 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 181] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 180] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 183] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 182] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 179] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 178] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 177] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 176] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 178] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 177] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 179] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 176] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 183] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 180] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 182] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 181] Device mesh: dp_size=32, cp_size=8, dp_rank=22, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 182] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 183] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 181] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 180] CP initialized via device mesh
[Gloo] Rank 184 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 185 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank [Gloo] Rank 187 is connected to 186255 is connected to  peer ranks. Expected number of connected peer ranks is : 255255 peer ranks. 
Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 184] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 185] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 186] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 187] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 185] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 184] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 186] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 187] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 186] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 185] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 184] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 187] CP initialized via device mesh
[Gloo] Rank 188 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 189 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 190 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 189] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 188] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 191 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 190] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 191] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 188] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 190] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 189] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 191] Device mesh: dp_size=32, cp_size=8, dp_rank=23, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 190] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 189] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 191] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 188] CP initialized via device mesh
[Gloo] Rank 192 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 193 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 194 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 192] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 195 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 193] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 194] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 195] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 192] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 194] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 193] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 195] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 192] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 193] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 194] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 195] CP initialized via device mesh
[Gloo] Rank 196 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 197 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 198 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 196] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 198] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 197] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 199 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 199] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 198] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 196] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 198] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 196] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 197] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 197] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 199] Device mesh: dp_size=32, cp_size=8, dp_rank=24, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 199] CP initialized via device mesh
[Gloo] Rank 204 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 205 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 206 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 207 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 205] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 204] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 206] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 207] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 207] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 205] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 206] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 207] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 205] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 206] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 204] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=4
[Gloo] Rank 208 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 204] CP initialized via device mesh
[Gloo] Rank 209 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 210 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 208] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 209] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 210] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 211 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 211] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 210] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 208] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 209] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 211] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 210] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 208] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 209] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 211] CP initialized via device mesh
[Gloo] Rank 212 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 213 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 215 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 214 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 212] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 213] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 215] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 214] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 215] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 212] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 213] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 215] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 212] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 214] Device mesh: dp_size=32, cp_size=8, dp_rank=26, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 213] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 214] CP initialized via device mesh
[Gloo] Rank [Gloo] Rank 201 is connected to 200255 peer ranks.  is connected to Expected number of connected peer ranks is : 255255
 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 202 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 203 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 201] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 200] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 202] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 203] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 200] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 201] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 202] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 203] Device mesh: dp_size=32, cp_size=8, dp_rank=25, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 201] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 200] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 202] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 203] CP initialized via device mesh
[Gloo] Rank 216 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 218 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 219 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 216] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 217 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 220 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 221 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 219] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 218] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 222 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 217] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 223 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 224 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 220] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 221] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 225 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 222] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 227 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 223] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 228 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 224] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 226 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 225] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 229 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 230 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 227] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 231 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 226] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 228] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 232 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 233 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 229] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 235 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 230] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 231] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 236 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 237 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 232] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 238 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 239 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 233] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 235] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 240 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 241 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 236] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 237] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 234 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 238] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 243 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 239] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 240] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 241] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 244 is connected to 255[Gloo] Rank  peer ranks. Expected number of connected peer ranks is : 245255 is connected to 255
 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 234] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 217] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 216] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 243] Distributed initialized: world_size=256, local_rank=3
[Gloo] Rank 246 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 245] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 248 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 244] Distributed initialized: world_size=256, local_rank=0
[Gloo] Rank 250 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 246] Distributed initialized: world_size=256, local_rank=2
[Gloo] Rank 252 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 253 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 247 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 217] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 216] CP initialized via device mesh
[Gloo] Rank 255 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 249 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[Gloo] Rank 251 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 218] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 247] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 252] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 248] Distributed initialized: world_size=256, local_rank=0
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 250] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 255] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 253] Distributed initialized: world_size=256, local_rank=1
[Gloo] Rank 242 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 223] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 222] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 221] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 220] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 251] Distributed initialized: world_size=256, local_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 249] Distributed initialized: world_size=256, local_rank=1
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 218] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 224] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 227] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 226] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 225] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 242] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 221] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 222] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 223] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 220] CP initialized via device mesh
[Gloo] Rank 254 is connected to 255 peer ranks. Expected number of connected peer ranks is : 255
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 227] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 224] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 226] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 225] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 219] Device mesh: dp_size=32, cp_size=8, dp_rank=27, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:120 - [Rank 254] Distributed initialized: world_size=256, local_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 231] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 229] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 232] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 234] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 233] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 235] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 219] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 231] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 229] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 232] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 234] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 233] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 239] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 237] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 238] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 236] Device mesh: dp_size=32, cp_size=8, dp_rank=29, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 235] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 230] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 240] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 241] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 243] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 239] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 238] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 237] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 236] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 230] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 240] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 241] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 242] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 243] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 247] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 245] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 246] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 244] Device mesh: dp_size=32, cp_size=8, dp_rank=30, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 242] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 247] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 245] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 244] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 246] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 254] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=6
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 252] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 253] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=5
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 248] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=0
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 251] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=3
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 249] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=1
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 250] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=2
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 254] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 252] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 228] Device mesh: dp_size=32, cp_size=8, dp_rank=28, cp_rank=4
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 251] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 248] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 249] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 250] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 253] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 228] CP initialized via device mesh
[2025-12-29 11:35:29] train_sft.py:146 - [Rank 255] Device mesh: dp_size=32, cp_size=8, dp_rank=31, cp_rank=7
[2025-12-29 11:35:29] train_sft.py:154 - [Rank 255] CP initialized via device mesh
wandb: Detected [openai] in use.
wandb: Use W&B Weave for improved LLM call tracing. Install Weave with `pip install weave` then add `import weave` to the top of your script.
wandb: For more information, check out the docs at: https://weave-docs.wandb.ai/
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 57] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:20] train_sft.py:218 - [Rank 59] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:20] train_sft.py:218 - [Rank 68] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:20] train_sft.py:218 - [Rank 172] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 115] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 107] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 137] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:20] train_sft.py:218 - [Rank 187] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 114] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 113] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 124] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 180] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 52] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 149] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 2] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 56] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 105] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 166] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 104] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 37] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 133] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 173] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 109] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:21] train_sft.py:218 - [Rank 71] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 179] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:21] train_sft.py:218 - [Rank 174] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 48] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 63] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 58] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 78] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 183] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 244] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 157] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 74] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 69] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 112] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 189] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 140] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 80] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 122] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 132] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 50] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 210] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 73] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 234] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:22] train_sft.py:218 - [Rank 228] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 214] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 192] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 135] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:22] train_sft.py:218 - [Rank 19] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 219] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 159] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 8] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 236] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 13] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 160] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 83] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 38] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 254] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 182] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 250] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 76] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 119] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 165] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 245] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 70] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 75] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 60] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 93] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 45] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 237] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 118] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 188] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 158] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 6] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 4] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 84] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 148] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 51] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 72] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 252] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 36] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 127] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 205] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 95] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 128] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 34] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 208] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 186] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:23] train_sft.py:218 - [Rank 87] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 25] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:23] train_sft.py:218 - [Rank 243] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 120] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 141] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 167] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 203] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 152] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 121] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 106] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 61] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 178] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 130] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 240] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 102] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 225] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 233] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 239] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 10] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 117] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 41] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 126] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 1] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 22] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 151] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 49] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 215] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 131] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 255] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 155] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 82] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 94] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 181] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 200] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 171] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 177] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:24] train_sft.py:218 - [Rank 212] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:24] train_sft.py:218 - [Rank 190] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 232] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 81] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 123] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 134] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 16] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 44] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 153] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 99] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 226] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 62] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 253] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 201] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 143] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 54] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 31] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 185] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 89] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 66] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 5] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 156] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 218] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 116] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 175] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 125] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 150] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 217] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 144] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 242] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 85] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 79] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 191] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 110] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 184] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 136] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 86] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 249] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 11] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:25] train_sft.py:218 - [Rank 176] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 15] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 229] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 196] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 39] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:25] train_sft.py:218 - [Rank 9] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 142] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 0] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 238] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 211] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 55] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 235] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 154] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 227] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 129] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 248] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 230] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 67] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 96] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 241] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 20] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 220] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 162] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 40] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 224] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 223] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 145] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 21] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 46] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 139] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 64] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 35] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 209] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 204] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 101] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 138] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 98] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 213] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 26] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 24] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 202] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 164] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:26] train_sft.py:218 - [Rank 90] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:26] train_sft.py:218 - [Rank 161] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 169] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 92] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 77] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 168] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 97] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 221] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 216] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 246] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 170] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 91] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 27] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 17] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 207] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 103] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 251] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 30] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 231] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 147] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 247] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 222] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 32] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 193] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 198] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 3] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 195] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 29] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 108] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 14] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 12] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 33] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 43] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 23] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 65] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 47] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 100] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:27] train_sft.py:218 - [Rank 28] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:27] train_sft.py:218 - [Rank 163] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 111] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 88] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 42] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 194] Data source initialized: dataset_size=7208, num_rollout=5000
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 206] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 7] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 199] Data source initialized: dataset_size=7208, num_rollout=5000

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
[2025-12-29 11:38:28] train_sft.py:218 - [Rank 146] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 18] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s][2025-12-29 11:38:28] train_sft.py:218 - [Rank 197] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.78s/it]
Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.27s/it][2025-12-29 11:38:29] train_sft.py:218 - [Rank 53] Data source initialized: dataset_size=7208, num_rollout=5000
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3ForCausalLM is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`
Flash Attention 2 only supports torch.float16 and torch.bfloat16 dtypes, but the current dype in Qwen3Model is torch.float32. You should run training or inference using Automatic Mixed-Precision via the `with torch.autocast(device_type='torch_device'):` decorator, or load the model with the `dtype` argument. Example: `model = AutoModel.from_pretrained("openai/whisper-tiny", attn_implementation="flash_attention_2", dtype=torch.float16)`

Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:18,  4.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:15,  3.80s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.33s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:17,  4.47s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.32s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.39s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.96s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:23,  5.79s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:17,  4.41s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.83s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:20,  5.01s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:21,  5.49s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.00s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.80s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.28s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.35s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.37s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.32s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.62s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.04s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:32,  8.20s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.06s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:13,  3.40s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:32,  8.14s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.25s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.08s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:19,  4.89s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.20s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.52s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.36s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:30,  7.63s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:32,  8.21s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:23,  5.97s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:27,  6.99s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:30,  7.52s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:29,  7.27s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.54s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.44s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.99s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.49s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:03<00:12,  3.19s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.34s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:18,  4.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:18,  4.68s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.77s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.23s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.04s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:20,  5.16s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.88s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:27,  6.79s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.45s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:19,  4.80s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.10s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:26,  6.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:23,  5.82s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:27,  6.89s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.18s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.21s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.72s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.68s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:22,  5.73s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:21,  5.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:21,  5.45s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:20,  5.24s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:32,  8.05s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:04<00:17,  4.49s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:22,  5.66s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.37s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.20s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.76s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.45s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.13s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:22,  5.73s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.26s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:26,  6.52s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.58s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:05<00:20,  5.03s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.65s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.13s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.17s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.30s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:32,  8.02s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.46s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.39s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:27,  6.82s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.94s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.68s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.39s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.77s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.95s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.44s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:30,  7.53s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:24,  6.23s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.89s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.22s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.19s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.33s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.84s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.63s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.59s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.16s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.22s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.17s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.99s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:27,  6.80s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.59s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.98s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:46, 11.63s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.67s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.52s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:26,  6.67s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.59s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.08s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.91s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:50, 12.74s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:53, 13.36s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:50, 12.69s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.30s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.59s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.39s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:25,  6.48s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.32s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.81s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.35s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:46, 11.67s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:30,  7.68s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.26s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.43s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.85s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.86s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.05s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.69s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.28s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.33s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.44s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.16s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.90s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.03s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.99s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.03s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.04s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:06<00:26,  6.64s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.14s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.65s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.86s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.95s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.24s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.83s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:53, 13.26s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:14<00:59, 14.86s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:55, 13.77s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:15<01:00, 15.11s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:52, 13.18s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:14<00:57, 14.46s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:51, 12.95s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:14<00:58, 14.65s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:14<00:57, 14.29s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.54s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.46s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.08s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:28,  7.23s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.95s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:30,  7.72s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.08s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.73s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.18s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.03s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.24s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.47s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.04s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.62s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.37s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.89s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:50, 12.50s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.39s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:52, 13.11s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:32,  8.02s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.29s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:07<00:31,  7.77s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:37,  9.49s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.28s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.32s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:50, 12.74s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.88s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:50, 12.69s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.03s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.40s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.98s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.50s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.45s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.84s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.14s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.84s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.63s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.21s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.53s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.14s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.50s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:33,  8.39s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.08s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.53s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:46, 11.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:51, 12.86s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:14<00:56, 14.05s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:55, 13.76s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.18s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:51, 13.00s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:53, 13.26s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.06s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:52, 13.20s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 12.00s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.61s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:46, 11.65s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:41, 10.31s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.43s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:42, 10.55s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.31s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:52, 13.02s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.88s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:40, 10.23s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.84s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 10.79s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.13s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.14s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:51, 12.90s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:50, 12.59s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:46, 11.52s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:54, 13.69s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:53, 13.48s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.63s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.36s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:35,  8.90s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:47, 11.80s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:08<00:34,  8.54s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:36,  9.04s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:39,  9.98s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:44, 11.06s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:48, 12.05s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:10<00:43, 11.00s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:09<00:38,  9.73s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:11<00:45, 11.38s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.33s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:54, 13.55s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:51, 12.79s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:13<00:54, 13.70s/it]
Loading checkpoint shards:  20%|██        | 1/5 [00:12<00:49, 12.48s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:31, 10.56s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:14<00:23,  7.96s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:15<00:25,  8.58s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:14<00:24,  8.11s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:31, 10.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:16<00:26,  8.79s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:14<00:23,  7.99s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:17<00:28,  9.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:14<00:23,  7.86s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:17<00:28,  9.64s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:30, 10.01s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.59s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:31, 10.56s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.17s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:32, 10.91s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:15<00:26,  8.81s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:15<00:26,  8.67s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.46s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.75s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:29,  9.91s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.90s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.48s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.80s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:29,  9.97s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:30, 10.16s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.41s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.61s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.40s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:17<00:28,  9.42s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:16<00:27,  9.05s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:31, 10.64s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:17<00:28,  9.42s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.49s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:31, 10.51s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.98s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.11s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.97s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.60s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:32, 10.80s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.76s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.32s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:30, 10.23s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.41s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.45s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.58s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.75s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:36, 12.24s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:17<00:29,  9.71s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:30, 10.11s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.44s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.55s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:29,  9.89s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.74s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.86s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:30, 10.01s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:17<00:29,  9.70s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.63s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.35s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.12s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.05s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.71s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.31s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.04s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.09s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.62s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:30, 10.15s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.82s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.71s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:30, 10.27s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.37s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:36, 12.22s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.51s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.84s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.49s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.24s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.56s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.42s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:33, 11.20s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.99s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:37, 12.38s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.95s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.56s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.53s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.20s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.17s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.98s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:31, 10.41s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.18s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:19<00:30, 10.23s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.07s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.78s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.44s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.45s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.48s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.77s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.88s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.78s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.31s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.22s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:18<00:31, 10.36s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.84s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.45s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.85s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.37s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.77s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.65s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.14s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.71s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:33, 11.07s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.13s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.16s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:33, 11.06s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.83s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:33, 11.22s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.24s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:40, 13.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.22s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.11s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.43s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.97s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.78s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.86s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.16s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.88s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.30s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.92s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.93s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.94s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.26s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.70s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.45s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.65s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:33, 11.19s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.07s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.07s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.46s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.74s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.47s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.81s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.71s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.93s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.13s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:36, 12.19s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:34, 11.51s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:36, 12.12s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:36, 12.25s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.20s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:37, 12.54s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.70s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:20<00:32, 10.76s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:35, 11.96s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.73s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.97s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:37, 12.37s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.17s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.09s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.79s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.33s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:21<00:34, 11.50s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.36s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.46s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.81s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:28<00:43, 14.43s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:28<00:43, 14.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:29<00:43, 14.52s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.32s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.23s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.79s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:41, 13.84s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.85s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.45s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.85s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.97s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.08s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.08s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.41s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.95s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.72s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.52s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.92s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.90s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.38s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:29<00:44, 14.92s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:28<00:43, 14.47s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:30<00:45, 15.02s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:28<00:42, 14.22s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:41, 13.68s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.63s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:41, 13.83s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.29s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.40s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.61s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.32s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.13s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.98s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.41s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.56s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.86s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.18s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.28s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:41, 13.80s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:41, 13.67s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.15s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 12.00s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.12s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:41, 13.78s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.10s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:41, 13.89s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:28<00:42, 14.16s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:28<00:42, 14.31s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.75s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.63s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.45s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.21s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.57s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.13s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:22<00:35, 11.76s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:41, 13.70s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.47s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.34s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.99s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.56s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.67s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.73s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:39, 13.09s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:25<00:38, 12.83s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.47s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.32s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.08s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:36, 12.26s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.39s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.74s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:37, 12.58s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:37, 12.52s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:24<00:38, 12.84s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:23<00:37, 12.35s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.57s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:41, 13.70s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:27<00:40, 13.66s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:40, 13.35s/it]
Loading checkpoint shards:  40%|████      | 2/5 [00:26<00:39, 13.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:22, 11.19s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:29<00:20, 10.48s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:28<00:20, 10.18s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:26<00:19,  9.77s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:28<00:21, 10.56s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.92s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:28<00:21, 10.51s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:29<00:21, 10.87s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:23, 11.96s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:22, 11.37s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:23, 11.69s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.56s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.02s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:22, 11.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:26<00:20, 10.03s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:30<00:22, 11.02s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:22, 11.46s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.71s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.18s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:22, 11.30s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:22, 11.32s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.55s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.69s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.53s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.58s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:23, 11.97s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.66s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:24, 12.01s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:30<00:22, 11.44s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:30<00:22, 11.37s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:24, 12.47s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.69s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.76s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.55s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:30<00:22, 11.15s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.51s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.19s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:22, 11.36s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.91s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.60s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:23, 11.85s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:23, 11.68s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:23, 11.88s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:31<00:23, 11.57s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.99s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:24, 12.03s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.83s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.60s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.03s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.66s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.64s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.64s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.53s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.16s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.21s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.66s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.52s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:32<00:23, 11.62s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.77s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.56s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.02s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.90s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.01s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.32s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.53s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.15s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.23s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.30s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.71s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:25, 12.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.51s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.00s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.64s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.42s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.87s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.06s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.39s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.96s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.47s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:25, 12.67s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:25, 12.90s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.63s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.17s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.61s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.12s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.37s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.78s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.33s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.94s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.90s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.40s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.08s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.78s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.49s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.27s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.21s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:24, 12.34s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.80s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:24, 12.15s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:24, 12.03s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.96s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.92s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.14s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.94s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.08s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.43s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:26, 13.47s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.07s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.63s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.22s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.32s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.33s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.65s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:24, 12.26s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.66s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.73s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.71s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:23, 11.63s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.90s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.63s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.58s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.39s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:33<00:24, 12.13s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.92s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.42s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.36s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.32s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.02s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.76s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.06s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.60s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.28s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.76s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.23s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.57s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:34<00:24, 12.43s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.58s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.30s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.22s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.67s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.08s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.21s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.75s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.89s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.93s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:24, 12.29s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.84s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:26, 13.38s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.29s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.69s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:25, 12.81s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.04s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.16s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.67s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:35<00:09,  9.32s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:38<00:09,  9.77s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:41<00:10, 10.21s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:36<00:09,  9.61s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.23s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.02s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.09s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.69s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.16s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.15s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.79s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:42<00:27, 13.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.51s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:27, 13.80s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:42<00:27, 13.92s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.75s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.01s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.13s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.61s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.61s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.96s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.14s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.34s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.76s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.63s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.95s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.36s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:28, 14.03s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:35<00:25, 12.60s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.56s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:26, 13.50s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.26s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.23s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.92s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.79s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.86s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.77s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.10s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:26, 13.04s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.72s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.24s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.80s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.18s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.41s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:27, 13.85s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.80s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.35s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.58s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:24, 12.48s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.69s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.14s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.28s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.72s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.67s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:25, 12.96s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:28, 14.10s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:26, 13.04s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.87s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:28, 14.19s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.57s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.99s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:27, 13.56s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.36s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.98s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.58s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.68s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.78s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:42<00:28, 14.22s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:42<00:28, 14.17s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:27, 13.97s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:28, 14.05s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:44<00:30, 15.02s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:43<00:29, 14.77s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:43<00:29, 14.62s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.66s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.29s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.21s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:45<00:30, 15.10s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:26, 13.43s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:39<00:10, 10.85s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.51s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:38<00:10, 10.66s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:39<00:10, 10.71s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:36<00:25, 12.84s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.12s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:37<00:25, 12.95s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.51s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:39<00:27, 13.61s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:27, 13.51s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.96s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:26, 13.38s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:40<00:00,  7.43s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:40<00:00,  8.08s/it]
[2025-12-29 11:39:06] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:41<00:00,  7.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:41<00:00,  8.36s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:44<00:00,  7.83s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:44<00:00,  8.99s/it]
[2025-12-29 11:39:06] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:06] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:38<00:00,  7.27s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:38<00:00,  7.75s/it]
[2025-12-29 11:39:06] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:41<00:10, 10.88s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.40s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:40<00:10, 10.77s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:37<00:10, 10.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.94s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:28, 14.15s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.87s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:38<00:27, 13.60s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.43s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.35s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.24s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.64s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.38s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.31s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.71s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:42<00:28, 14.18s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:40<00:27, 13.92s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:41<00:28, 14.01s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:42<00:11, 11.20s/it]
Loading checkpoint shards:  60%|██████    | 3/5 [00:42<00:28, 14.25s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.63s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.92s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.37s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.51s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:42<00:10, 10.88s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:40<00:10, 10.67s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:41<00:10, 10.77s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.31s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:41<00:11, 11.41s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.85s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.25s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.28s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.76s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:42<00:11, 11.02s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.22s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.22s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.65s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:41<00:11, 11.37s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.60s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.68s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:42<00:11, 11.34s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:42<00:11, 11.35s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.00s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:41<00:11, 11.25s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.29s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.64s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.13s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.16s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.02s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.86s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:44<00:00,  8.60s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:43<00:00,  8.47s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:44<00:00,  8.92s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:43<00:00,  8.71s/it]
[2025-12-29 11:39:10] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:10] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:10] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:43<00:00,  8.50s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:43<00:00,  8.75s/it]
[2025-12-29 11:39:10] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.97s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.21s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.49s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.86s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.63s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.10s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.97s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.86s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.37s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.35s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.50s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.91s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:41<00:00,  8.18s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:41<00:00,  8.40s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.64s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:45<00:00,  8.56s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.21s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:45<00:00,  9.04s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.15s/it][2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  8.96s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.97s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.80s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.93s/it][2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.71s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.60s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.95s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  8.83s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.16s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.89s/it]

[2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.07s/it][2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.78s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.77s/it]
[2025-12-29 11:39:11] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.98s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.88s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.44s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.14s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.96s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.38s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.42s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.80s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.09s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.63s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.96s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.91s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.93s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.17s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.43s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.85s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.54s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.09s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.21s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.78s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.15s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.94s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.24s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.63s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.70s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.89s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.64s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.29s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.74s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.68s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.09s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.16s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.52s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.35s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.04s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.27s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.04s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.97s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.95s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.84s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.14s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.79s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.55s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.80s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.62s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.83s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.04s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.67s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.15s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.84s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.29s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.64s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:43<00:11, 11.43s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.19s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:44<00:11, 11.59s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.93s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.67s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.53s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.71s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.58s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.63s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.32s/it]
[2025-12-29 11:39:12] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:12] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:12] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.93s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.12s/it]
[2025-12-29 11:39:12] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.81s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.04s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.79s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.12s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.95s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.20s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.47s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.28s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.51s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:12, 12.08s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.25s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.91s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:44<00:00,  8.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:44<00:00,  8.97s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.41s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.28s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  8.67s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.89s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:45<00:00,  8.35s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:45<00:00,  9.12s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.81s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.85s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.53s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.02s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.91s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.92s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.70s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.34s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.71s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.21s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.35s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.74s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.12s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.05s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.47s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.35s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.75s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:11, 11.90s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.64s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.10s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:11, 11.99s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.87s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.10s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.45s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.49s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.55s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.32s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.86s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.14s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.45s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.60s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.20s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.53s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.58s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.48s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:12, 12.08s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.38s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.45s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.13s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.55s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.51s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:12, 12.03s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:12, 12.12s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:45<00:00,  8.73s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:45<00:00,  9.15s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.77s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.99s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.63s/it]

[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.34s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.40s/it]
[2025-12-29 11:39:13] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.16s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.14s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:45<00:11, 11.95s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.40s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.20s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.89s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.91s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.19s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.39s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.33s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.89s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.15s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.98s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.66s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.16s/it][2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.39s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.06s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.61s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.02s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.75s/it]
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.61s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.48s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.83s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.38s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.23s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.47s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.25s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.34s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.52s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.73s/it]

[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.93s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.77s/it]
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.56s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.25s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.48s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.45s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.67s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.82s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.87s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.36s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.30s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.80s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.32s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.25s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.38s/it][2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  8.88s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:46<00:00,  9.38s/it]
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.23s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.46s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.31s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.50s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.55s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:12, 12.09s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.01s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.18s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.28s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.15s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.21s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.09s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.89s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.59s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.07s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.33s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.11s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.13s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.32s/it]


Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:12, 12.16s/it][2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.35s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.66s/it]
[2025-12-29 11:39:14] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.38s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:12, 12.14s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.61s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.37s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.41s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.30s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.35s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.08s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.34s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.26s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.58s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.78s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.04s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.08s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.18s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.40s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:47<00:11, 11.94s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.09s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.22s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.85s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.54s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.18s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.84s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.32s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.50s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.94s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.74s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:46<00:11, 11.84s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.74s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.23s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.12s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.60s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.69s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.57s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.72s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.64s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.43s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.71s/it][2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.22s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.56s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.14s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.29s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.37s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.70s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.06s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.17s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.75s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.02s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.12s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.61s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.80s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.10s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.09s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.26s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.30s/it]

[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.24s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.60s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.42s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.44s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.81s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.41s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.31s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.51s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.02s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.88s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.96s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.30s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.75s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.52s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.08s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.95s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.89s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.56s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.82s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.65s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  8.94s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.92s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.50s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.99s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.01s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.12s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.17s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.17s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.02s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.95s/it]

[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.32s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.62s/it]
[2025-12-29 11:39:15] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.04s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.19s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.25s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.33s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.67s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.86s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.23s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.61s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.44s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.59s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.03s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.02s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.65s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  8.81s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.72s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.71s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.01s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.49s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.20s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.04s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:48<00:00,  9.65s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.62s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.93s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.02s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.95s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.19s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.61s/it]

[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.06s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.31s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  8.71s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:47<00:00,  9.59s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  8.97s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.13s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.18s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.53s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.73s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.99s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.13s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.54s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.44s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.37s/it][2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.33s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.16s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.53s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.53s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:53<00:13, 13.03s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.92s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:53<00:13, 13.08s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.74s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:53<00:12, 12.96s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.38s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.41s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.50s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.62s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.45s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.35s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.42s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.27s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.43s/it]

[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.62s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.48s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.21s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.88s/it]


Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.40s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.67s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.90s/it]
[2025-12-29 11:39:16] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:54<00:13, 13.34s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:55<00:13, 13.43s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:56<00:13, 13.59s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:56<00:13, 13.62s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.91s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.86s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.73s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.48s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.98s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.70s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.43s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.34s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.60s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.83s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.67s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.57s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.23s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.78s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.09s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.46s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.41s/it]

[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.11s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:49<00:00,  9.85s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.37s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.42s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.23s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.11s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.51s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.75s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.22s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.10s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.39s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.88s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.52s/it]

[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.38s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.47s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.01s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.17s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.16s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.39s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.03s/it]


Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.37s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.38s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.87s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.25s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.40s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.43s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.30s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.81s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.52s/it]

[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.53s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.01s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.81s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.35s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.75s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.51s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.23s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.66s/it]

[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.75s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.21s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.16s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.66s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.21s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.44s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.77s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.00s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.28s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.19s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.14s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.45s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.07s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.19s/it]


Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.78s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.21s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.20s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.88s/it][2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.37s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:48<00:12, 12.51s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.56s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00,  9.57s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.53s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00, 11.40s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.32s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.43s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.07s/it]
[2025-12-29 11:39:17] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:50<00:12, 12.68s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:49<00:12, 12.63s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.98s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.81s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.88s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.04s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.26s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.62s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.14s/it]


Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.06s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.19s/it]
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.46s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.06s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.17s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.30s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.52s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.11s/it]

[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.51s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.99s/it]
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.42s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.92s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.29s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.34s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.62s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.71s/it]

[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.46s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.00s/it]
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.31s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.12s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.58s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.45s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.43s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.76s/it]

[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.44s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.39s/it]
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.38s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.51s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.59s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.33s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.37s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.96s/it]

[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.69s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.18s/it]
[2025-12-29 11:39:18] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.52s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.90s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.22s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.20s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.25s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.28s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.35s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.90s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.18s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.77s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.50s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.32s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.44s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.82s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.96s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.65s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.95s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.60s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.28s/it]

[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.57s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.73s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.76s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.51s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.57s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.71s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.66s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.93s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.06s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.63s/it][2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.87s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00,  9.25s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:50<00:00, 10.19s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.28s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.24s/it]

[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.40s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.53s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.24s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.66s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.40s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.42s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.05s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.09s/it]

[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.26s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.71s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.82s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.70s/it]
Loading checkpoint shards:  80%|████████  | 4/5 [00:51<00:12, 12.64s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.35s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.82s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.38s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.48s/it]

Loading checkpoint shards:  80%|████████  | 4/5 [00:52<00:12, 12.82s/it][2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.43s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.01s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.33s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.25s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.61s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.81s/it]


Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00,  9.08s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:51<00:00, 10.22s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.17s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.40s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.74s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.28s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.63s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.02s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.84s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.73s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.23s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.74s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.29s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.72s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.43s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.63s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.23s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.56s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.02s/it]
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.59s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.08s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.72s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.46s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.78s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.36s/it]

[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:19] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.70s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.29s/it]
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.44s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.61s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.64s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.07s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.40s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.53s/it]
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00,  9.42s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:52<00:00, 10.57s/it]
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00,  9.86s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00, 11.58s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:58<00:00,  9.89s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00,  9.78s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:58<00:00, 11.64s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00, 11.40s/it]

[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00,  9.80s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00, 11.46s/it]
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:20] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:58<00:00, 10.10s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [01:00<00:00, 10.26s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:58<00:00, 11.79s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [01:00<00:00, 10.27s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [01:00<00:00, 12.14s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [01:00<00:00, 12.19s/it]
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.70s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.00s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:59<00:00, 10.17s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:59<00:00, 11.92s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.84s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.78s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.35s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.20s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.60s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.97s/it]
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.56s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.70s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.20s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.87s/it]


Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.82s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.29s/it]
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.53s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.79s/it]
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.57s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.80s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.61s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.51s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.66s/it]
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.75s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.21s/it]
[2025-12-29 11:39:21] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.64s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.89s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.79s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.58s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.23s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.74s/it]
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00,  9.55s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:53<00:00, 10.66s/it]
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.73s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.11s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.76s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00,  9.62s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.19s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:54<00:00, 10.86s/it]

[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.85s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.37s/it]
[2025-12-29 11:39:22] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00,  9.75s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00, 11.45s/it]

Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00,  9.74s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00,  9.67s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:56<00:00, 11.27s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:57<00:00, 11.42s/it]

[2025-12-29 11:39:23] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:23] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:23] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32

Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00,  9.64s/it]
Loading checkpoint shards: 100%|██████████| 5/5 [00:55<00:00, 11.19s/it]
[2025-12-29 11:39:23] actor.py:1109 - FSDP MixedPrecision Policy: param_dtype=torch.bfloat16, reduce_dtype=torch.float32
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 24] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 25] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 19] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 16] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 17] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 18] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 26] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 27] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 4] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 5] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 79] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 176] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 6] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 76] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 173] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 175] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 7] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 10] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 122] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 142] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 96] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 178] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 97] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 193] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 106] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 104] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 143] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 47] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 99] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 195] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 194] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 8] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 46] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 37] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 105] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 35] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 33] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 34] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 111] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 103] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 102] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 109] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 32] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 83] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 36] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 120] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 110] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 80] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 128] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 108] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 107] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 101] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 179] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 11] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 130] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 100] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 82] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 63] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 45] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 162] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 163] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 129] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 121] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 136] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 90] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 14] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 192] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 60] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 78] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 12] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 77] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 13] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 139] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 38] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 39] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 123] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 141] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 88] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 161] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 70] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 2] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 1] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 89] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 131] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 49] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 50] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 98] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 44] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 235] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 72] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 81] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 91] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 61] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 234] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 233] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 68] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 172] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 62] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 75] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 71] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 3] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 137] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 196] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 48] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 189] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 177] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 226] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 51] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 132] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 134] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 225] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 191] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 174] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 152] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 84] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 188] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 133] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 190] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 87] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 140] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 15] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 227] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 69] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 199] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 153] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 197] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 224] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 232] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 160] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 9] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 86] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 115] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 113] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 155] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 112] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 198] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 85] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 138] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 74] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 114] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 148] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 154] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 73] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 158] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 149] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 22] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 23] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 21] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 151] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 20] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 150] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 211] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 127] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 208] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 209] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 157] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 126] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 236] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 251] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 147] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 145] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 164] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 237] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 146] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 156] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 210] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 169] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 144] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 167] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 186] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 165] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 240] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 168] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 218] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 231] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 171] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 216] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 170] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 31] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 229] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 228] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 187] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 248] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 217] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 29] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 221] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 243] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 230] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 184] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 249] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 219] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 242] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 185] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 241] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 200] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 201] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 159] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 124] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 222] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 55] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 52] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 28] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 92] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 95] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 250] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 180] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 182] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 66] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 64] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 181] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 183] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 166] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 245] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 244] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 117] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 238] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 119] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 239] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 94] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 125] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 67] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 59] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 202] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 203] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 57] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 247] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 246] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 40] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 65] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 41] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 220] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 93] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 118] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 116] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 53] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 58] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 42] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 43] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 54] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 135] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 205] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 204] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 255] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 254] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 206] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 215] Model initialized with FSDP
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 213] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 207] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 252] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 214] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 253] Model initialized with FSDP
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 223] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 212] Model initialized with FSDP
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 30] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:25] train_sft.py:322 - [Rank 56] Model initialized with FSDP
[2025-12-29 11:39:25] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:25] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:30] train_sft.py:322 - [Rank 0] Model initialized with FSDP
[2025-12-29 11:39:30] lr_scheduler.py:80 - > learning rate decay style: WSD
[2025-12-29 11:39:30] checkpoint.py:123 - [FSDP] No tracker file at /p/scratch/envcomp/mihir/huggingface/Qwen3-8B/latest_checkpointed_iteration.txt; skipping load.
/p/project1/hai_anysensor/mahajan1/miles/.venv/lib/python3.12/site-packages/torch/distributed/distributed_c10d.py:4807: UserWarning: No device id is provided via `init_process_group` or `barrier `. Using the current device set by the user. 
  warnings.warn(  # warn only once
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 2] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 3] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 0] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 2] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 3] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 179] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 178] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 176] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 217] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 218] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 1] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 150] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 72] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 73] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 65] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 0] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 177] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 155] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 154] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 153] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 152] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 34] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 140] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 133] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 132] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 134] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 103] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 102] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 100] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 31] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 214] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 18] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 19] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 16] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 27] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 25] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 178] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 179] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 176] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 240] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 155] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 154] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 111] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 108] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 55] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 54] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 135] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 228] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 229] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 231] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 166] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 47] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 44] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 188] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 191] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 189] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 202] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 200] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 203] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 74] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 186] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 187] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 184] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 11] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 8] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 9] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 10] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 215] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 1] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 234] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 232] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 18] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 24] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 25] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 27] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 177] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 242] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 153] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 63] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 149] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 55] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 54] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 134] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 132] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 133] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 103] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 102] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 79] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 78] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 165] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 164] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 157] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 156] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 117] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 118] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 116] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 119] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 45] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 248] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 72] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 73] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 49] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 51] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 48] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 113] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 115] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 212] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 214] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 82] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 83] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 81] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 17] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 19] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 24] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 121] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 120] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 123] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 41] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 43] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 40] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 131] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 130] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 138] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 136] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 139] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 137] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 97] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 99] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 96] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 98] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 89] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 88] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 90] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 91] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 241] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 152] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 35] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 160] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 63] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 61] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 148] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 150] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 126] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 127] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 124] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 143] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 109] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 110] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 174] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 52] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 135] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 231] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 228] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 229] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 12] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 100] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 199] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 196] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 252] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 255] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 20] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 22] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 95] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 93] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 86] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 245] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 246] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 244] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 29] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 221] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 222] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 220] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 183] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 180] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 166] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 5] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 47] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 44] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 205] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 207] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 236] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 188] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 191] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 189] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 39] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 38] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 36] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 74] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 106] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 226] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 58] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 186] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 192] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 195] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 194] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 168] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 170] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 211] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 209] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 11] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 8] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 9] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 66] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 144] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 145] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 147] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 68] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 69] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 215] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 216] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 233] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 82] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 16] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 26] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 122] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 121] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 120] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 43] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 41] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 40] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 131] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 130] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 138] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 136] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 99] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 97] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 89] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 88] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 240] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 243] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 34] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 160] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 61] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 149] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 148] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 126] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 141] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 111] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 172] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 175] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 53] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 230] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 12] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 101] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 199] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 252] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 23] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 20] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 21] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 92] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 84] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 76] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 78] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 79] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 246] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 245] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 28] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 30] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 31] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 223] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 183] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 164] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 165] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 157] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 118] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 4] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 6] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 45] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 206] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 204] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 238] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 236] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 190] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 38] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 39] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 202] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 251] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 75] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 104] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 49] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 48] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 51] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 225] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 58] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 184] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 187] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 195] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 171] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 168] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 112] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 208] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 10] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 64] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 67] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 146] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 70] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 212] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 217] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 234] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 83] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 81] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 17] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 26] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 123] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 42] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 128] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 129] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 137] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 139] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 96] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 91] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 90] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 242] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 32] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 162] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 161] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 163] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 60] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 151] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 125] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 140] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 108] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 174] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 52] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 230] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 14] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 13] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 101] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 198] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 253] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 22] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 95] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 86] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 77] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 247] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 29] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 221] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 222] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 220] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 181] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 180] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 167] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 158] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 156] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 117] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 5] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 46] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 205] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 207] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 239] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 190] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 36] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 37] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 200] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 248] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 75] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 105] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 50] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 224] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 226] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 57] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 185] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 192] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 170] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 113] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 209] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 211] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 65] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 145] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 144] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 68] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 213] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 218] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 235] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 232] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 80] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 122] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 42] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 128] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 129] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 98] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 241] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 35] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 162] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 161] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 163] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 60] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 151] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 127] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 124] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 143] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 109] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 110] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 172] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 53] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 15] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 196] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 254] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 255] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 23] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 21] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 93] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 84] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 76] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 244] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 28] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 30] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 223] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 182] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 167] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 159] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 116] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 119] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 7] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 46] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 206] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 204] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 237] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 37] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 203] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 251] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 250] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 107] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 106] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 50] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 225] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 56] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 59] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 185] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 194] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 169] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 115] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 208] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 210] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 66] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 147] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 71] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 213] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 216] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 233] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 80] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 243] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 33] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 62] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 125] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 142] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 175] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 14] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 13] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 197] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 253] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 94] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 85] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 77] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 247] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 181] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 158] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 4] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 6] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 238] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 201] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 249] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 104] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 227] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 224] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 57] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 193] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 171] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 114] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 210] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 64] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 146] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 69] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 219] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 235] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 32] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 62] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 141] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 173] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 15] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 198] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 254] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 92] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:102 - [Rank 87] SFTTrainer initialized successfully
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 182] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 159] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 7] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 239] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 201] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 250] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 249] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 105] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 227] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 59] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 56] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 193] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 169] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 112] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 67] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 70] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 219] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 33] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 142] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 173] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 197] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 94] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 85] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 237] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 107] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 114] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 71] Starting training: rollout_id 0 -> 5000
[2025-12-29 11:39:30] train_sft.py:702 - [Rank 87] Starting training: rollout_id 0 -> 5000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s][2025-12-29 11:39:30] train_sft.py:376 - SFT rollout sample: tokens_len=3986, response_length=3898

actor_val:   1%|          | 1/100 [00:06<10:57,  6.64s/it][2025-12-29 11:39:37] train_sft.py:376 - SFT rollout sample: tokens_len=3980, response_length=3892

actor_val:   2%|▏         | 2/100 [00:08<06:30,  3.98s/it][2025-12-29 11:39:39] train_sft.py:376 - SFT rollout sample: tokens_len=3557, response_length=1416

actor_val:   3%|▎         | 3/100 [00:10<04:39,  2.88s/it][2025-12-29 11:39:40] train_sft.py:376 - SFT rollout sample: tokens_len=2923, response_length=783

actor_val:   4%|▍         | 4/100 [00:11<03:48,  2.38s/it][2025-12-29 11:39:42] train_sft.py:376 - SFT rollout sample: tokens_len=3730, response_length=3391

actor_val:   5%|▌         | 5/100 [00:13<03:19,  2.10s/it][2025-12-29 11:39:43] train_sft.py:376 - SFT rollout sample: tokens_len=2890, response_length=2410

actor_val:   6%|▌         | 6/100 [00:15<03:00,  1.92s/it][2025-12-29 11:39:45] train_sft.py:376 - SFT rollout sample: tokens_len=3402, response_length=1261

actor_val:   7%|▋         | 7/100 [00:16<02:48,  1.81s/it][2025-12-29 11:39:47] train_sft.py:376 - SFT rollout sample: tokens_len=3054, response_length=913

actor_val:   8%|▊         | 8/100 [00:18<02:39,  1.74s/it][2025-12-29 11:39:48] train_sft.py:376 - SFT rollout sample: tokens_len=3319, response_length=2933

actor_val:   9%|▉         | 9/100 [00:19<02:32,  1.68s/it][2025-12-29 11:39:50] train_sft.py:376 - SFT rollout sample: tokens_len=3884, response_length=1743

actor_val:  10%|█         | 10/100 [00:21<02:28,  1.65s/it][2025-12-29 11:39:51] train_sft.py:376 - SFT rollout sample: tokens_len=3707, response_length=1566

actor_val:  11%|█         | 11/100 [00:22<02:24,  1.63s/it][2025-12-29 11:39:53] train_sft.py:376 - SFT rollout sample: tokens_len=3607, response_length=3519

actor_val:  12%|█▏        | 12/100 [00:24<02:21,  1.61s/it][2025-12-29 11:39:54] train_sft.py:376 - SFT rollout sample: tokens_len=1014, response_length=926

actor_val:  13%|█▎        | 13/100 [00:26<02:18,  1.59s/it][2025-12-29 11:39:56] train_sft.py:376 - SFT rollout sample: tokens_len=3994, response_length=3596

actor_val:  14%|█▍        | 14/100 [00:27<02:16,  1.59s/it][2025-12-29 11:39:58] train_sft.py:376 - SFT rollout sample: tokens_len=4056, response_length=3657

actor_val:  15%|█▌        | 15/100 [00:29<02:14,  1.59s/it][2025-12-29 11:39:59] train_sft.py:376 - SFT rollout sample: tokens_len=3301, response_length=2903

actor_val:  16%|█▌        | 16/100 [00:30<02:12,  1.58s/it][2025-12-29 11:40:01] train_sft.py:376 - SFT rollout sample: tokens_len=2155, response_length=2067

actor_val:  17%|█▋        | 17/100 [00:32<02:10,  1.57s/it][2025-12-29 11:40:02] train_sft.py:376 - SFT rollout sample: tokens_len=3078, response_length=937

actor_val:  18%|█▊        | 18/100 [00:33<02:08,  1.57s/it][2025-12-29 11:40:04] train_sft.py:376 - SFT rollout sample: tokens_len=4054, response_length=1913

actor_val:  19%|█▉        | 19/100 [00:35<02:07,  1.57s/it][2025-12-29 11:40:05] train_sft.py:376 - SFT rollout sample: tokens_len=2916, response_length=2495

actor_val:  20%|██        | 20/100 [00:37<02:05,  1.57s/it][2025-12-29 11:40:07] train_sft.py:376 - SFT rollout sample: tokens_len=2528, response_length=387

actor_val:  21%|██        | 21/100 [00:38<02:04,  1.57s/it][2025-12-29 11:40:09] train_sft.py:376 - SFT rollout sample: tokens_len=3350, response_length=1209

actor_val:  22%|██▏       | 22/100 [00:40<02:02,  1.57s/it][2025-12-29 11:40:10] train_sft.py:376 - SFT rollout sample: tokens_len=3561, response_length=1420

actor_val:  23%|██▎       | 23/100 [00:41<02:00,  1.56s/it][2025-12-29 11:40:12] train_sft.py:376 - SFT rollout sample: tokens_len=3004, response_length=2430

actor_val:  24%|██▍       | 24/100 [00:43<01:58,  1.56s/it][2025-12-29 11:40:13] train_sft.py:376 - SFT rollout sample: tokens_len=3020, response_length=2695

actor_val:  25%|██▌       | 25/100 [00:44<01:57,  1.57s/it][2025-12-29 11:40:15] train_sft.py:376 - SFT rollout sample: tokens_len=2910, response_length=2403

actor_val:  26%|██▌       | 26/100 [00:46<01:56,  1.57s/it][2025-12-29 11:40:16] train_sft.py:376 - SFT rollout sample: tokens_len=3019, response_length=2673

actor_val:  27%|██▋       | 27/100 [00:48<01:55,  1.58s/it][2025-12-29 11:40:18] train_sft.py:376 - SFT rollout sample: tokens_len=593, response_length=252

actor_val:  28%|██▊       | 28/100 [00:49<01:53,  1.58s/it][2025-12-29 11:40:20] train_sft.py:376 - SFT rollout sample: tokens_len=4052, response_length=1911

actor_val:  29%|██▉       | 29/100 [00:51<01:51,  1.57s/it][2025-12-29 11:40:21] train_sft.py:376 - SFT rollout sample: tokens_len=3837, response_length=1696

actor_val:  30%|███       | 30/100 [00:52<01:50,  1.57s/it][2025-12-29 11:40:23] train_sft.py:376 - SFT rollout sample: tokens_len=2890, response_length=2410

actor_val:  31%|███       | 31/100 [00:54<01:48,  1.57s/it][2025-12-29 11:40:24] train_sft.py:376 - SFT rollout sample: tokens_len=4006, response_length=3918

actor_val:  32%|███▏      | 32/100 [00:55<01:46,  1.57s/it][2025-12-29 11:40:26] train_sft.py:376 - SFT rollout sample: tokens_len=3663, response_length=3325

actor_val:  33%|███▎      | 33/100 [00:57<01:45,  1.57s/it][2025-12-29 11:40:27] train_sft.py:376 - SFT rollout sample: tokens_len=3526, response_length=1385

actor_val:  34%|███▍      | 34/100 [00:59<01:43,  1.56s/it][2025-12-29 11:40:29] train_sft.py:376 - SFT rollout sample: tokens_len=4012, response_length=3633

actor_val:  35%|███▌      | 35/100 [01:00<01:42,  1.58s/it][2025-12-29 11:40:31] train_sft.py:376 - SFT rollout sample: tokens_len=3639, response_length=1627

actor_val:  36%|███▌      | 36/100 [01:02<01:41,  1.58s/it][2025-12-29 11:40:32] train_sft.py:376 - SFT rollout sample: tokens_len=2895, response_length=2519

actor_val:  37%|███▋      | 37/100 [01:03<01:39,  1.59s/it][2025-12-29 11:40:34] train_sft.py:376 - SFT rollout sample: tokens_len=2722, response_length=2634

actor_val:  38%|███▊      | 38/100 [01:05<01:38,  1.58s/it][2025-12-29 11:40:35] train_sft.py:376 - SFT rollout sample: tokens_len=3473, response_length=1332

actor_val:  39%|███▉      | 39/100 [01:07<01:36,  1.58s/it][2025-12-29 11:40:37] train_sft.py:376 - SFT rollout sample: tokens_len=2812, response_length=2724

actor_val:  40%|████      | 40/100 [01:08<01:33,  1.57s/it][2025-12-29 11:40:38] train_sft.py:376 - SFT rollout sample: tokens_len=3496, response_length=2476

actor_val:  41%|████      | 41/100 [01:10<01:32,  1.57s/it][2025-12-29 11:40:40] train_sft.py:376 - SFT rollout sample: tokens_len=2999, response_length=858

actor_val:  42%|████▏     | 42/100 [01:11<01:31,  1.57s/it][2025-12-29 11:40:42] train_sft.py:376 - SFT rollout sample: tokens_len=3190, response_length=2468

actor_val:  43%|████▎     | 43/100 [01:13<01:29,  1.57s/it][2025-12-29 11:40:43] train_sft.py:376 - SFT rollout sample: tokens_len=3742, response_length=1601

actor_val:  44%|████▍     | 44/100 [01:14<01:27,  1.56s/it][2025-12-29 11:40:45] train_sft.py:376 - SFT rollout sample: tokens_len=3896, response_length=3365

actor_val:  45%|████▌     | 45/100 [01:16<01:25,  1.55s/it][2025-12-29 11:40:46] train_sft.py:376 - SFT rollout sample: tokens_len=3073, response_length=932

actor_val:  46%|████▌     | 46/100 [01:17<01:23,  1.55s/it][2025-12-29 11:40:48] train_sft.py:376 - SFT rollout sample: tokens_len=3361, response_length=1220

actor_val:  47%|████▋     | 47/100 [01:19<01:22,  1.56s/it][2025-12-29 11:40:49] train_sft.py:376 - SFT rollout sample: tokens_len=2764, response_length=623

actor_val:  48%|████▊     | 48/100 [01:20<01:20,  1.55s/it][2025-12-29 11:40:51] train_sft.py:376 - SFT rollout sample: tokens_len=2804, response_length=663

actor_val:  49%|████▉     | 49/100 [01:22<01:19,  1.56s/it][2025-12-29 11:40:52] train_sft.py:376 - SFT rollout sample: tokens_len=4037, response_length=2568

actor_val:  50%|█████     | 50/100 [01:24<01:18,  1.57s/it][2025-12-29 11:40:54] train_sft.py:376 - SFT rollout sample: tokens_len=4025, response_length=1884

actor_val:  51%|█████     | 51/100 [01:25<01:16,  1.57s/it][2025-12-29 11:40:56] train_sft.py:376 - SFT rollout sample: tokens_len=3038, response_length=2950

actor_val:  52%|█████▏    | 52/100 [01:27<01:16,  1.59s/it][2025-12-29 11:40:57] train_sft.py:376 - SFT rollout sample: tokens_len=2714, response_length=573

actor_val:  53%|█████▎    | 53/100 [01:28<01:14,  1.57s/it][2025-12-29 11:40:59] train_sft.py:376 - SFT rollout sample: tokens_len=2886, response_length=1179

actor_val:  54%|█████▍    | 54/100 [01:30<01:11,  1.56s/it][2025-12-29 11:41:00] train_sft.py:376 - SFT rollout sample: tokens_len=3467, response_length=1326

actor_val:  55%|█████▌    | 55/100 [01:32<01:10,  1.57s/it][2025-12-29 11:41:02] train_sft.py:376 - SFT rollout sample: tokens_len=4023, response_length=3495

actor_val:  56%|█████▌    | 56/100 [01:33<01:09,  1.58s/it][2025-12-29 11:41:03] train_sft.py:376 - SFT rollout sample: tokens_len=3202, response_length=2832

actor_val:  57%|█████▋    | 57/100 [01:35<01:07,  1.57s/it][2025-12-29 11:41:05] train_sft.py:376 - SFT rollout sample: tokens_len=2738, response_length=597

actor_val:  58%|█████▊    | 58/100 [01:36<01:05,  1.57s/it][2025-12-29 11:41:07] train_sft.py:376 - SFT rollout sample: tokens_len=2641, response_length=500

actor_val:  59%|█████▉    | 59/100 [01:38<01:04,  1.56s/it][2025-12-29 11:41:08] train_sft.py:376 - SFT rollout sample: tokens_len=3094, response_length=953

actor_val:  60%|██████    | 60/100 [01:39<01:02,  1.56s/it][2025-12-29 11:41:10] train_sft.py:376 - SFT rollout sample: tokens_len=4061, response_length=1920

actor_val:  61%|██████    | 61/100 [01:41<01:01,  1.57s/it][2025-12-29 11:41:11] train_sft.py:376 - SFT rollout sample: tokens_len=3318, response_length=1177

actor_val:  62%|██████▏   | 62/100 [01:42<00:59,  1.57s/it][2025-12-29 11:41:13] train_sft.py:376 - SFT rollout sample: tokens_len=3977, response_length=1836

actor_val:  63%|██████▎   | 63/100 [01:44<00:58,  1.57s/it][2025-12-29 11:41:14] train_sft.py:376 - SFT rollout sample: tokens_len=3713, response_length=3329

actor_val:  64%|██████▍   | 64/100 [01:46<00:56,  1.57s/it][2025-12-29 11:41:16] train_sft.py:376 - SFT rollout sample: tokens_len=2722, response_length=2634

actor_val:  65%|██████▌   | 65/100 [01:47<00:54,  1.56s/it][2025-12-29 11:41:18] train_sft.py:376 - SFT rollout sample: tokens_len=2991, response_length=850

actor_val:  66%|██████▌   | 66/100 [01:49<00:52,  1.56s/it][2025-12-29 11:41:19] train_sft.py:376 - SFT rollout sample: tokens_len=3828, response_length=3281

actor_val:  67%|██████▋   | 67/100 [01:50<00:51,  1.55s/it][2025-12-29 11:41:21] train_sft.py:376 - SFT rollout sample: tokens_len=3282, response_length=3194

actor_val:  68%|██████▊   | 68/100 [01:52<00:49,  1.55s/it][2025-12-29 11:41:22] train_sft.py:376 - SFT rollout sample: tokens_len=4072, response_length=3019

actor_val:  69%|██████▉   | 69/100 [01:53<00:48,  1.56s/it][2025-12-29 11:41:24] train_sft.py:376 - SFT rollout sample: tokens_len=4082, response_length=1941

actor_val:  70%|███████   | 70/100 [01:55<00:46,  1.56s/it][2025-12-29 11:41:25] train_sft.py:376 - SFT rollout sample: tokens_len=3953, response_length=1812

actor_val:  71%|███████   | 71/100 [01:57<00:45,  1.57s/it][2025-12-29 11:41:27] train_sft.py:376 - SFT rollout sample: tokens_len=3770, response_length=1629

actor_val:  72%|███████▏  | 72/100 [01:58<00:43,  1.56s/it][2025-12-29 11:41:28] train_sft.py:376 - SFT rollout sample: tokens_len=3813, response_length=3468

actor_val:  73%|███████▎  | 73/100 [02:00<00:42,  1.57s/it][2025-12-29 11:41:30] train_sft.py:376 - SFT rollout sample: tokens_len=3713, response_length=3329

actor_val:  74%|███████▍  | 74/100 [02:01<00:40,  1.56s/it][2025-12-29 11:41:32] train_sft.py:376 - SFT rollout sample: tokens_len=2847, response_length=706

actor_val:  75%|███████▌  | 75/100 [02:03<00:38,  1.56s/it][2025-12-29 11:41:33] train_sft.py:376 - SFT rollout sample: tokens_len=2911, response_length=770

actor_val:  76%|███████▌  | 76/100 [02:04<00:37,  1.56s/it][2025-12-29 11:41:35] train_sft.py:376 - SFT rollout sample: tokens_len=4077, response_length=1936

actor_val:  77%|███████▋  | 77/100 [02:06<00:35,  1.55s/it][2025-12-29 11:41:36] train_sft.py:376 - SFT rollout sample: tokens_len=4069, response_length=1928

actor_val:  78%|███████▊  | 78/100 [02:07<00:34,  1.55s/it][2025-12-29 11:41:38] train_sft.py:376 - SFT rollout sample: tokens_len=2863, response_length=2469

actor_val:  79%|███████▉  | 79/100 [02:09<00:32,  1.54s/it][2025-12-29 11:41:39] train_sft.py:376 - SFT rollout sample: tokens_len=2851, response_length=2534

actor_val:  80%|████████  | 80/100 [02:11<00:30,  1.55s/it][2025-12-29 11:41:41] train_sft.py:376 - SFT rollout sample: tokens_len=3356, response_length=1313

actor_val:  81%|████████  | 81/100 [02:12<00:29,  1.55s/it][2025-12-29 11:41:42] train_sft.py:376 - SFT rollout sample: tokens_len=3127, response_length=2626

actor_val:  82%|████████▏ | 82/100 [02:14<00:28,  1.56s/it][2025-12-29 11:41:44] train_sft.py:376 - SFT rollout sample: tokens_len=4071, response_length=1930

actor_val:  83%|████████▎ | 83/100 [02:15<00:26,  1.55s/it][2025-12-29 11:41:46] train_sft.py:376 - SFT rollout sample: tokens_len=3835, response_length=2526

actor_val:  84%|████████▍ | 84/100 [02:17<00:25,  1.57s/it][2025-12-29 11:41:47] train_sft.py:376 - SFT rollout sample: tokens_len=2722, response_length=2634

actor_val:  85%|████████▌ | 85/100 [02:18<00:23,  1.56s/it][2025-12-29 11:41:49] train_sft.py:376 - SFT rollout sample: tokens_len=3730, response_length=3391

actor_val:  86%|████████▌ | 86/100 [02:20<00:21,  1.56s/it][2025-12-29 11:41:50] train_sft.py:376 - SFT rollout sample: tokens_len=4066, response_length=3978

actor_val:  87%|████████▋ | 87/100 [02:21<00:20,  1.55s/it][2025-12-29 11:41:52] train_sft.py:376 - SFT rollout sample: tokens_len=3896, response_length=3365

actor_val:  88%|████████▊ | 88/100 [02:23<00:18,  1.54s/it][2025-12-29 11:41:53] train_sft.py:376 - SFT rollout sample: tokens_len=3761, response_length=1621

actor_val:  89%|████████▉ | 89/100 [02:25<00:17,  1.55s/it][2025-12-29 11:41:55] train_sft.py:376 - SFT rollout sample: tokens_len=4072, response_length=1932

actor_val:  90%|█████████ | 90/100 [02:26<00:15,  1.57s/it][2025-12-29 11:41:57] train_sft.py:376 - SFT rollout sample: tokens_len=2660, response_length=519

actor_val:  91%|█████████ | 91/100 [02:28<00:14,  1.57s/it][2025-12-29 11:41:58] train_sft.py:376 - SFT rollout sample: tokens_len=3900, response_length=3812

actor_val:  92%|█████████▏| 92/100 [02:29<00:12,  1.56s/it][2025-12-29 11:42:00] train_sft.py:376 - SFT rollout sample: tokens_len=3038, response_length=2950

actor_val:  93%|█████████▎| 93/100 [02:31<00:10,  1.56s/it][2025-12-29 11:42:01] train_sft.py:376 - SFT rollout sample: tokens_len=3845, response_length=3430

actor_val:  94%|█████████▍| 94/100 [02:32<00:09,  1.55s/it][2025-12-29 11:42:03] train_sft.py:376 - SFT rollout sample: tokens_len=2861, response_length=720

actor_val:  95%|█████████▌| 95/100 [02:34<00:07,  1.55s/it][2025-12-29 11:42:04] train_sft.py:376 - SFT rollout sample: tokens_len=3496, response_length=3097

actor_val:  96%|█████████▌| 96/100 [02:35<00:06,  1.57s/it][2025-12-29 11:42:06] train_sft.py:376 - SFT rollout sample: tokens_len=3134, response_length=2788

actor_val:  97%|█████████▋| 97/100 [02:37<00:04,  1.56s/it][2025-12-29 11:42:07] train_sft.py:376 - SFT rollout sample: tokens_len=2829, response_length=688

actor_val:  98%|█████████▊| 98/100 [02:39<00:03,  1.57s/it][2025-12-29 11:42:09] train_sft.py:376 - SFT rollout sample: tokens_len=3634, response_length=1493

actor_val:  99%|█████████▉| 99/100 [02:40<00:01,  1.57s/it][2025-12-29 11:42:11] train_sft.py:376 - SFT rollout sample: tokens_len=2768, response_length=628

actor_val: 100%|██████████| 100/100 [02:42<00:00,  1.57s/it]
actor_val: 100%|██████████| 100/100 [02:42<00:00,  1.62s/it]
[2025-12-29 11:42:13] train_sft.py:658 - step 0: {'val/loss': 3.210625}
[2025-12-29 11:42:13] train_sft.py:376 - SFT rollout sample: tokens_len=3581, response_length=1440
[2025-12-29 11:42:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:23] train_sft.py:596 - step 0: {'train/loss': 3.327880859375, 'train/grad_norm': 59.24150848388672, 'train/lr_0': 4e-08}

actor_train: 100%|██████████| 1/1 [00:10<00:00, 10.47s/it]
actor_train: 100%|██████████| 1/1 [00:10<00:00, 10.47s/it]
[2025-12-29 11:42:23] timer.py:32 - Timer actor_train end (elapsed: 10.5s)
[2025-12-29 11:42:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:28] train_sft.py:596 - step 1: {'train/loss': 3.120361328125, 'train/grad_norm': 53.861053466796875, 'train/lr_0': 8e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 11:42:28] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 11:42:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:33] train_sft.py:596 - step 2: {'train/loss': 3.142333984375, 'train/grad_norm': 55.37569046020508, 'train/lr_0': 1.2000000000000002e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 11:42:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 11:42:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:38] train_sft.py:596 - step 3: {'train/loss': 3.0672607421875, 'train/grad_norm': 56.11806869506836, 'train/lr_0': 1.6e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 11:42:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:42:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:43] train_sft.py:596 - step 4: {'train/loss': 3.027587890625, 'train/grad_norm': 52.72954177856445, 'train/lr_0': 2.0000000000000002e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 11:42:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:42:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:48] train_sft.py:596 - step 5: {'train/loss': 3.0784912109375, 'train/grad_norm': 54.6158561706543, 'train/lr_0': 2.4000000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 11:42:48] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:42:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:53] train_sft.py:596 - step 6: {'train/loss': 3.3447265625, 'train/grad_norm': 58.24888229370117, 'train/lr_0': 2.8e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 11:42:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:42:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:42:58] train_sft.py:596 - step 7: {'train/loss': 3.093505859375, 'train/grad_norm': 52.936187744140625, 'train/lr_0': 3.2e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 11:42:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:42:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:03] train_sft.py:596 - step 8: {'train/loss': 3.261474609375, 'train/grad_norm': 61.549354553222656, 'train/lr_0': 3.6000000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 11:43:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:43:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:08] train_sft.py:596 - step 9: {'train/loss': 3.07958984375, 'train/grad_norm': 55.18781280517578, 'train/lr_0': 4.0000000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:43:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:43:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:13] train_sft.py:596 - step 10: {'train/loss': 3.261962890625, 'train/grad_norm': 56.67993927001953, 'train/lr_0': 4.4e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.21s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.21s/it]
[2025-12-29 11:43:13] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 11:43:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:18] train_sft.py:596 - step 11: {'train/loss': 3.225341796875, 'train/grad_norm': 58.222469329833984, 'train/lr_0': 4.800000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 11:43:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:43:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:23] train_sft.py:596 - step 12: {'train/loss': 2.729248046875, 'train/grad_norm': 49.16128158569336, 'train/lr_0': 5.200000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:43:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:43:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:28] train_sft.py:596 - step 13: {'train/loss': 2.895751953125, 'train/grad_norm': 54.88449478149414, 'train/lr_0': 5.6e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 11:43:28] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:43:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:33] train_sft.py:596 - step 14: {'train/loss': 2.99560546875, 'train/grad_norm': 54.19515609741211, 'train/lr_0': 6.000000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 11:43:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:43:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:39] train_sft.py:596 - step 15: {'train/loss': 2.82275390625, 'train/grad_norm': 51.5958137512207, 'train/lr_0': 6.4e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.41s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.41s/it]
[2025-12-29 11:43:39] timer.py:32 - Timer actor_train end (elapsed: 5.4s)
[2025-12-29 11:43:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:44] train_sft.py:596 - step 16: {'train/loss': 2.608642578125, 'train/grad_norm': 51.611751556396484, 'train/lr_0': 6.800000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 11:43:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:43:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:49] train_sft.py:596 - step 17: {'train/loss': 2.714599609375, 'train/grad_norm': 52.95711898803711, 'train/lr_0': 7.200000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 11:43:49] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:43:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:54] train_sft.py:596 - step 18: {'train/loss': 2.61572265625, 'train/grad_norm': 49.428768157958984, 'train/lr_0': 7.6e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 11:43:54] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:43:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:43:59] train_sft.py:596 - step 19: {'train/loss': 2.626220703125, 'train/grad_norm': 48.944313049316406, 'train/lr_0': 8.000000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 11:43:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:43:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:04] train_sft.py:596 - step 20: {'train/loss': 2.81396484375, 'train/grad_norm': 52.49983215332031, 'train/lr_0': 8.4e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.33s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.33s/it]
[2025-12-29 11:44:04] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 11:44:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:10] train_sft.py:596 - step 21: {'train/loss': 1.86572265625, 'train/grad_norm': 35.17095947265625, 'train/lr_0': 8.8e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.29s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.29s/it]
[2025-12-29 11:44:10] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 11:44:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:15] train_sft.py:596 - step 22: {'train/loss': 1.7724609375, 'train/grad_norm': 36.77425003051758, 'train/lr_0': 9.2e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 11:44:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:44:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:20] train_sft.py:596 - step 23: {'train/loss': 1.692138671875, 'train/grad_norm': 37.59327697753906, 'train/lr_0': 9.600000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.30s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.30s/it]
[2025-12-29 11:44:20] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 11:44:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:25] train_sft.py:596 - step 24: {'train/loss': 1.4149169921875, 'train/grad_norm': 33.13627624511719, 'train/lr_0': 1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:44:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:44:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:30] train_sft.py:596 - step 25: {'train/loss': 1.447998046875, 'train/grad_norm': 33.67497634887695, 'train/lr_0': 1.0400000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 11:44:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:44:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:35] train_sft.py:596 - step 26: {'train/loss': 1.3487548828125, 'train/grad_norm': 33.65550994873047, 'train/lr_0': 1.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 11:44:35] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:44:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:40] train_sft.py:596 - step 27: {'train/loss': 1.2479248046875, 'train/grad_norm': 32.91498947143555, 'train/lr_0': 1.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 11:44:40] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:44:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:45] train_sft.py:596 - step 28: {'train/loss': 0.98016357421875, 'train/grad_norm': 16.306676864624023, 'train/lr_0': 1.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 11:44:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:44:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:49] train_sft.py:596 - step 29: {'train/loss': 0.91943359375, 'train/grad_norm': 13.242607116699219, 'train/lr_0': 1.2000000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:44:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:44:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:54] train_sft.py:596 - step 30: {'train/loss': 0.83831787109375, 'train/grad_norm': 6.389670372009277, 'train/lr_0': 1.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 11:44:54] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:44:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:44:59] train_sft.py:596 - step 31: {'train/loss': 0.97802734375, 'train/grad_norm': 6.44702672958374, 'train/lr_0': 1.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 11:44:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:44:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:04] train_sft.py:596 - step 32: {'train/loss': 0.624114990234375, 'train/grad_norm': 3.640784978866577, 'train/lr_0': 1.3200000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 11:45:04] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:45:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:09] train_sft.py:596 - step 33: {'train/loss': 0.700347900390625, 'train/grad_norm': 6.684250831604004, 'train/lr_0': 1.3600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 11:45:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 11:45:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:13] train_sft.py:596 - step 34: {'train/loss': 0.908905029296875, 'train/grad_norm': 5.75960636138916, 'train/lr_0': 1.4000000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:45:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:18] train_sft.py:596 - step 35: {'train/loss': 0.6650390625, 'train/grad_norm': 3.5941545963287354, 'train/lr_0': 1.4400000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 11:45:18] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:45:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:23] train_sft.py:596 - step 36: {'train/loss': 0.776763916015625, 'train/grad_norm': 3.586038589477539, 'train/lr_0': 1.4800000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:45:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:28] train_sft.py:596 - step 37: {'train/loss': 0.6915283203125, 'train/grad_norm': 5.220402717590332, 'train/lr_0': 1.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 11:45:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:45:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:32] train_sft.py:596 - step 38: {'train/loss': 0.69598388671875, 'train/grad_norm': 3.6860275268554688, 'train/lr_0': 1.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:45:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:37] train_sft.py:596 - step 39: {'train/loss': 0.7216796875, 'train/grad_norm': 3.303467035293579, 'train/lr_0': 1.6000000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 11:45:37] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:45:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:42] train_sft.py:596 - step 40: {'train/loss': 0.825927734375, 'train/grad_norm': 3.915839433670044, 'train/lr_0': 1.6400000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:45:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:47] train_sft.py:596 - step 41: {'train/loss': 0.70166015625, 'train/grad_norm': 3.0443990230560303, 'train/lr_0': 1.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 11:45:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:51] train_sft.py:596 - step 42: {'train/loss': 0.59490966796875, 'train/grad_norm': 2.360225200653076, 'train/lr_0': 1.7200000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:45:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:45:56] train_sft.py:596 - step 43: {'train/loss': 0.6376953125, 'train/grad_norm': 2.792353630065918, 'train/lr_0': 1.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:45:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:45:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:01] train_sft.py:596 - step 44: {'train/loss': 0.60418701171875, 'train/grad_norm': 2.4959123134613037, 'train/lr_0': 1.8000000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:46:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:05] train_sft.py:596 - step 45: {'train/loss': 0.5731201171875, 'train/grad_norm': 2.0646841526031494, 'train/lr_0': 1.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:46:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:10] train_sft.py:596 - step 46: {'train/loss': 0.540496826171875, 'train/grad_norm': 2.0807981491088867, 'train/lr_0': 1.8800000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:46:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:15] train_sft.py:596 - step 47: {'train/loss': 0.601409912109375, 'train/grad_norm': 2.7351176738739014, 'train/lr_0': 1.9200000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 11:46:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:46:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:19] train_sft.py:596 - step 48: {'train/loss': 0.50579833984375, 'train/grad_norm': 1.7198309898376465, 'train/lr_0': 1.9600000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 11:46:19] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:46:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:24] train_sft.py:596 - step 49: {'train/loss': 0.619354248046875, 'train/grad_norm': 2.309314727783203, 'train/lr_0': 2e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:46:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:29] train_sft.py:596 - step 50: {'train/loss': 0.565673828125, 'train/grad_norm': 2.6785380840301514, 'train/lr_0': 2.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:46:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:33] train_sft.py:596 - step 51: {'train/loss': 0.546966552734375, 'train/grad_norm': 1.8471014499664307, 'train/lr_0': 2.0800000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:46:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:38] train_sft.py:596 - step 52: {'train/loss': 0.64825439453125, 'train/grad_norm': 3.103832960128784, 'train/lr_0': 2.1200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 11:46:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:46:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:43] train_sft.py:596 - step 53: {'train/loss': 0.4530029296875, 'train/grad_norm': 2.953427791595459, 'train/lr_0': 2.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:46:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:47] train_sft.py:596 - step 54: {'train/loss': 0.6982421875, 'train/grad_norm': 3.1844851970672607, 'train/lr_0': 2.2e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:46:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:52] train_sft.py:596 - step 55: {'train/loss': 0.445892333984375, 'train/grad_norm': 1.6583293676376343, 'train/lr_0': 2.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:46:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:46:57] train_sft.py:596 - step 56: {'train/loss': 0.437652587890625, 'train/grad_norm': 1.86904776096344, 'train/lr_0': 2.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:46:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:46:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:01] train_sft.py:596 - step 57: {'train/loss': 0.542083740234375, 'train/grad_norm': 1.9479529857635498, 'train/lr_0': 2.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 11:47:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:47:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:06] train_sft.py:596 - step 58: {'train/loss': 0.5504608154296875, 'train/grad_norm': 1.5808764696121216, 'train/lr_0': 2.3600000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:47:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:10] train_sft.py:596 - step 59: {'train/loss': 0.59783935546875, 'train/grad_norm': 1.7850139141082764, 'train/lr_0': 2.4000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:47:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:15] train_sft.py:596 - step 60: {'train/loss': 0.50836181640625, 'train/grad_norm': 2.3403172492980957, 'train/lr_0': 2.4400000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:47:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:20] train_sft.py:596 - step 61: {'train/loss': 0.487518310546875, 'train/grad_norm': 2.4253358840942383, 'train/lr_0': 2.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:47:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:25] train_sft.py:596 - step 62: {'train/loss': 0.52398681640625, 'train/grad_norm': 1.73463773727417, 'train/lr_0': 2.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:47:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:29] train_sft.py:596 - step 63: {'train/loss': 0.544219970703125, 'train/grad_norm': 1.9599932432174683, 'train/lr_0': 2.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:47:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:34] train_sft.py:596 - step 64: {'train/loss': 0.502410888671875, 'train/grad_norm': 2.2329554557800293, 'train/lr_0': 2.6000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:47:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:39] train_sft.py:596 - step 65: {'train/loss': 0.42535400390625, 'train/grad_norm': 1.5814340114593506, 'train/lr_0': 2.6400000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 11:47:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:43] train_sft.py:596 - step 66: {'train/loss': 0.4610595703125, 'train/grad_norm': 1.9851791858673096, 'train/lr_0': 2.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:47:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:48] train_sft.py:596 - step 67: {'train/loss': 0.481414794921875, 'train/grad_norm': 1.7795765399932861, 'train/lr_0': 2.7200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 11:47:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:52] train_sft.py:596 - step 68: {'train/loss': 0.589569091796875, 'train/grad_norm': 3.0755462646484375, 'train/lr_0': 2.7600000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 11:47:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:47:57] train_sft.py:596 - step 69: {'train/loss': 0.50634765625, 'train/grad_norm': 2.386873483657837, 'train/lr_0': 2.8000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 11:47:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:47:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:02] train_sft.py:596 - step 70: {'train/loss': 0.440948486328125, 'train/grad_norm': 2.6658895015716553, 'train/lr_0': 2.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:48:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:06] train_sft.py:596 - step 71: {'train/loss': 0.535125732421875, 'train/grad_norm': 2.0994462966918945, 'train/lr_0': 2.8800000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:48:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:11] train_sft.py:596 - step 72: {'train/loss': 0.4020843505859375, 'train/grad_norm': 2.1338140964508057, 'train/lr_0': 2.9200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 11:48:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:16] train_sft.py:596 - step 73: {'train/loss': 0.538482666015625, 'train/grad_norm': 2.682997703552246, 'train/lr_0': 2.9600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:48:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:20] train_sft.py:596 - step 74: {'train/loss': 0.366363525390625, 'train/grad_norm': 2.4085257053375244, 'train/lr_0': 3e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:48:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:25] train_sft.py:596 - step 75: {'train/loss': 0.368865966796875, 'train/grad_norm': 1.588249683380127, 'train/lr_0': 3.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:48:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:30] train_sft.py:596 - step 76: {'train/loss': 0.4383544921875, 'train/grad_norm': 2.143242359161377, 'train/lr_0': 3.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:48:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:34] train_sft.py:596 - step 77: {'train/loss': 0.43048095703125, 'train/grad_norm': 4.169734001159668, 'train/lr_0': 3.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:48:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:48:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:39] train_sft.py:596 - step 78: {'train/loss': 0.360443115234375, 'train/grad_norm': 1.8490548133850098, 'train/lr_0': 3.1600000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:48:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:43] train_sft.py:596 - step 79: {'train/loss': 0.502197265625, 'train/grad_norm': 1.6401715278625488, 'train/lr_0': 3.2000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 11:48:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:48:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:48] train_sft.py:596 - step 80: {'train/loss': 0.350494384765625, 'train/grad_norm': 1.6943845748901367, 'train/lr_0': 3.2400000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:48:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:53] train_sft.py:596 - step 81: {'train/loss': 0.439361572265625, 'train/grad_norm': 1.8806836605072021, 'train/lr_0': 3.2800000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 11:48:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:48:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:48:58] train_sft.py:596 - step 82: {'train/loss': 0.4140625, 'train/grad_norm': 1.9165256023406982, 'train/lr_0': 3.3200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 11:48:58] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:48:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:02] train_sft.py:596 - step 83: {'train/loss': 0.47564697265625, 'train/grad_norm': 3.0449066162109375, 'train/lr_0': 3.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:49:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:07] train_sft.py:596 - step 84: {'train/loss': 0.36077880859375, 'train/grad_norm': 1.5617567300796509, 'train/lr_0': 3.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:49:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:12] train_sft.py:596 - step 85: {'train/loss': 0.40496826171875, 'train/grad_norm': 1.4331218004226685, 'train/lr_0': 3.4400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:49:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:16] train_sft.py:596 - step 86: {'train/loss': 0.499114990234375, 'train/grad_norm': 2.476301908493042, 'train/lr_0': 3.4800000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:49:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:21] train_sft.py:596 - step 87: {'train/loss': 0.384857177734375, 'train/grad_norm': 1.9893641471862793, 'train/lr_0': 3.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:49:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:26] train_sft.py:596 - step 88: {'train/loss': 0.3521728515625, 'train/grad_norm': 1.8021372556686401, 'train/lr_0': 3.5600000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 11:49:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:49:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:30] train_sft.py:596 - step 89: {'train/loss': 0.355621337890625, 'train/grad_norm': 1.4979177713394165, 'train/lr_0': 3.6000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 11:49:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:49:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:35] train_sft.py:596 - step 90: {'train/loss': 0.3547821044921875, 'train/grad_norm': 1.6526516675949097, 'train/lr_0': 3.6400000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:49:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:49:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:39] train_sft.py:596 - step 91: {'train/loss': 0.402069091796875, 'train/grad_norm': 1.6909676790237427, 'train/lr_0': 3.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 11:49:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:49:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:44] train_sft.py:596 - step 92: {'train/loss': 0.2982330322265625, 'train/grad_norm': 1.2740885019302368, 'train/lr_0': 3.7200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:49:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:49] train_sft.py:596 - step 93: {'train/loss': 0.38800048828125, 'train/grad_norm': 2.1567695140838623, 'train/lr_0': 3.7600000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:49:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:53] train_sft.py:596 - step 94: {'train/loss': 0.3358612060546875, 'train/grad_norm': 2.072216749191284, 'train/lr_0': 3.8000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:49:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:49:58] train_sft.py:596 - step 95: {'train/loss': 0.32916259765625, 'train/grad_norm': 1.568310022354126, 'train/lr_0': 3.8400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:49:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:49:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:03] train_sft.py:596 - step 96: {'train/loss': 0.4332275390625, 'train/grad_norm': 2.682419776916504, 'train/lr_0': 3.88e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:50:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:50:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:07] train_sft.py:596 - step 97: {'train/loss': 0.360382080078125, 'train/grad_norm': 1.4874809980392456, 'train/lr_0': 3.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:50:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:50:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:12] train_sft.py:596 - step 98: {'train/loss': 0.3376922607421875, 'train/grad_norm': 1.929118275642395, 'train/lr_0': 3.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 11:50:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:50:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:17] train_sft.py:596 - step 99: {'train/loss': 0.3183135986328125, 'train/grad_norm': 1.303816318511963, 'train/lr_0': 4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:50:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:50:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:21] train_sft.py:596 - step 100: {'train/loss': 0.519500732421875, 'train/grad_norm': 2.077904462814331, 'train/lr_0': 4.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 11:50:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:50:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:26] train_sft.py:596 - step 101: {'train/loss': 0.4658050537109375, 'train/grad_norm': 2.520035982131958, 'train/lr_0': 4.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 11:50:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:50:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:30] train_sft.py:596 - step 102: {'train/loss': 0.380950927734375, 'train/grad_norm': 2.3448145389556885, 'train/lr_0': 4.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:50:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:50:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:35] train_sft.py:596 - step 103: {'train/loss': 0.35992431640625, 'train/grad_norm': 2.145545721054077, 'train/lr_0': 4.160000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:50:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:50:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:40] train_sft.py:596 - step 104: {'train/loss': 0.41259765625, 'train/grad_norm': 1.4619594812393188, 'train/lr_0': 4.2000000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:50:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:50:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:44] train_sft.py:596 - step 105: {'train/loss': 0.35015869140625, 'train/grad_norm': 1.5612820386886597, 'train/lr_0': 4.240000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 11:50:44] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:50:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:49] train_sft.py:596 - step 106: {'train/loss': 0.444183349609375, 'train/grad_norm': 1.5163975954055786, 'train/lr_0': 4.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:50:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:50:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:54] train_sft.py:596 - step 107: {'train/loss': 0.399078369140625, 'train/grad_norm': 1.7484691143035889, 'train/lr_0': 4.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 11:50:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:50:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:50:58] train_sft.py:596 - step 108: {'train/loss': 0.397674560546875, 'train/grad_norm': 2.0064313411712646, 'train/lr_0': 4.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 11:50:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:50:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:03] train_sft.py:596 - step 109: {'train/loss': 0.2961578369140625, 'train/grad_norm': 1.647400975227356, 'train/lr_0': 4.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:51:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:08] train_sft.py:596 - step 110: {'train/loss': 0.3556060791015625, 'train/grad_norm': 1.9667428731918335, 'train/lr_0': 4.440000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:51:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:12] train_sft.py:596 - step 111: {'train/loss': 0.371002197265625, 'train/grad_norm': 2.3838517665863037, 'train/lr_0': 4.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:51:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:17] train_sft.py:596 - step 112: {'train/loss': 0.3861083984375, 'train/grad_norm': 1.6404285430908203, 'train/lr_0': 4.520000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:51:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:51:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:21] train_sft.py:596 - step 113: {'train/loss': 0.2730712890625, 'train/grad_norm': 1.6000254154205322, 'train/lr_0': 4.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:51:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:26] train_sft.py:596 - step 114: {'train/loss': 0.314727783203125, 'train/grad_norm': 1.5271388292312622, 'train/lr_0': 4.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:51:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:31] train_sft.py:596 - step 115: {'train/loss': 0.32159423828125, 'train/grad_norm': 1.6027965545654297, 'train/lr_0': 4.64e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 11:51:31] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:51:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:36] train_sft.py:596 - step 116: {'train/loss': 0.35888671875, 'train/grad_norm': 1.346030354499817, 'train/lr_0': 4.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:51:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:40] train_sft.py:596 - step 117: {'train/loss': 0.461395263671875, 'train/grad_norm': 2.0030086040496826, 'train/lr_0': 4.7200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:51:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:45] train_sft.py:596 - step 118: {'train/loss': 0.3516845703125, 'train/grad_norm': 1.7623907327651978, 'train/lr_0': 4.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:51:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:49] train_sft.py:596 - step 119: {'train/loss': 0.36920166015625, 'train/grad_norm': 2.0049688816070557, 'train/lr_0': 4.800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 11:51:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:51:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:54] train_sft.py:596 - step 120: {'train/loss': 0.300506591796875, 'train/grad_norm': 1.2686641216278076, 'train/lr_0': 4.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:51:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:51:59] train_sft.py:596 - step 121: {'train/loss': 0.3530731201171875, 'train/grad_norm': 1.807720422744751, 'train/lr_0': 4.880000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:51:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:51:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:03] train_sft.py:596 - step 122: {'train/loss': 0.26979827880859375, 'train/grad_norm': 1.7657705545425415, 'train/lr_0': 4.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:52:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:08] train_sft.py:596 - step 123: {'train/loss': 0.29583740234375, 'train/grad_norm': 1.544420599937439, 'train/lr_0': 4.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:52:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:13] train_sft.py:596 - step 124: {'train/loss': 0.401153564453125, 'train/grad_norm': 1.9666471481323242, 'train/lr_0': 5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:52:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:17] train_sft.py:596 - step 125: {'train/loss': 0.301116943359375, 'train/grad_norm': 1.429956316947937, 'train/lr_0': 5.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:52:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:22] train_sft.py:596 - step 126: {'train/loss': 0.3126068115234375, 'train/grad_norm': 1.7378170490264893, 'train/lr_0': 5.0800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:52:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:27] train_sft.py:596 - step 127: {'train/loss': 0.366455078125, 'train/grad_norm': 1.820188045501709, 'train/lr_0': 5.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 11:52:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:52:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:31] train_sft.py:596 - step 128: {'train/loss': 0.34344482421875, 'train/grad_norm': 1.4956241846084595, 'train/lr_0': 5.1600000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:52:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:36] train_sft.py:596 - step 129: {'train/loss': 0.28302001953125, 'train/grad_norm': 1.584973692893982, 'train/lr_0': 5.200000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:52:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:41] train_sft.py:596 - step 130: {'train/loss': 0.35052490234375, 'train/grad_norm': 1.5607662200927734, 'train/lr_0': 5.240000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:52:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:45] train_sft.py:596 - step 131: {'train/loss': 0.3782196044921875, 'train/grad_norm': 1.1914151906967163, 'train/lr_0': 5.280000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 11:52:45] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:52:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:50] train_sft.py:596 - step 132: {'train/loss': 0.337890625, 'train/grad_norm': 1.4949846267700195, 'train/lr_0': 5.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:52:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:55] train_sft.py:596 - step 133: {'train/loss': 0.3524932861328125, 'train/grad_norm': 2.143751382827759, 'train/lr_0': 5.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 11:52:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:52:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:52:59] train_sft.py:596 - step 134: {'train/loss': 0.3270263671875, 'train/grad_norm': 1.8400893211364746, 'train/lr_0': 5.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:52:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:04] train_sft.py:596 - step 135: {'train/loss': 0.29150390625, 'train/grad_norm': 1.263002634048462, 'train/lr_0': 5.4400000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:53:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:09] train_sft.py:596 - step 136: {'train/loss': 0.249755859375, 'train/grad_norm': 1.147660493850708, 'train/lr_0': 5.480000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 11:53:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:13] train_sft.py:596 - step 137: {'train/loss': 0.33526611328125, 'train/grad_norm': 2.3089518547058105, 'train/lr_0': 5.5200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:53:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:18] train_sft.py:596 - step 138: {'train/loss': 0.314117431640625, 'train/grad_norm': 1.58197820186615, 'train/lr_0': 5.560000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 11:53:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:53:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:23] train_sft.py:596 - step 139: {'train/loss': 0.327392578125, 'train/grad_norm': 1.7326278686523438, 'train/lr_0': 5.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:53:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:28] train_sft.py:596 - step 140: {'train/loss': 0.361328125, 'train/grad_norm': 1.6787995100021362, 'train/lr_0': 5.64e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:53:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:32] train_sft.py:596 - step 141: {'train/loss': 0.27203369140625, 'train/grad_norm': 1.7605054378509521, 'train/lr_0': 5.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 11:53:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:53:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:37] train_sft.py:596 - step 142: {'train/loss': 0.3106689453125, 'train/grad_norm': 1.8448703289031982, 'train/lr_0': 5.72e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 11:53:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:53:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:42] train_sft.py:596 - step 143: {'train/loss': 0.294647216796875, 'train/grad_norm': 0.941290020942688, 'train/lr_0': 5.760000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:53:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:46] train_sft.py:596 - step 144: {'train/loss': 0.308135986328125, 'train/grad_norm': 1.3574103116989136, 'train/lr_0': 5.8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 11:53:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 11:53:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:51] train_sft.py:596 - step 145: {'train/loss': 0.275604248046875, 'train/grad_norm': 1.5603601932525635, 'train/lr_0': 5.840000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:53:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:53:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:53:56] train_sft.py:596 - step 146: {'train/loss': 0.2502899169921875, 'train/grad_norm': 1.1896121501922607, 'train/lr_0': 5.8800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 11:53:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:53:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:00] train_sft.py:596 - step 147: {'train/loss': 0.362030029296875, 'train/grad_norm': 2.0279629230499268, 'train/lr_0': 5.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 11:54:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:54:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:05] train_sft.py:596 - step 148: {'train/loss': 0.3143768310546875, 'train/grad_norm': 1.3342318534851074, 'train/lr_0': 5.9600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 11:54:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:54:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:10] train_sft.py:596 - step 149: {'train/loss': 0.3102874755859375, 'train/grad_norm': 1.2094987630844116, 'train/lr_0': 6e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 11:54:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:54:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:15] train_sft.py:596 - step 150: {'train/loss': 0.30242919921875, 'train/grad_norm': 1.6205973625183105, 'train/lr_0': 6.040000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:54:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:54:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:19] train_sft.py:596 - step 151: {'train/loss': 0.2971954345703125, 'train/grad_norm': 1.50322425365448, 'train/lr_0': 6.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:54:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:54:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:24] train_sft.py:596 - step 152: {'train/loss': 0.3112945556640625, 'train/grad_norm': 1.3872145414352417, 'train/lr_0': 6.120000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 11:54:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:54:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:28] train_sft.py:596 - step 153: {'train/loss': 0.289794921875, 'train/grad_norm': 1.8043081760406494, 'train/lr_0': 6.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:54:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:54:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:33] train_sft.py:596 - step 154: {'train/loss': 0.2651519775390625, 'train/grad_norm': 1.1381003856658936, 'train/lr_0': 6.200000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 11:54:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:54:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:38] train_sft.py:596 - step 155: {'train/loss': 0.30804443359375, 'train/grad_norm': 1.6129200458526611, 'train/lr_0': 6.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:54:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:54:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:43] train_sft.py:596 - step 156: {'train/loss': 0.2854461669921875, 'train/grad_norm': 1.94221830368042, 'train/lr_0': 6.280000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 11:54:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:54:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:47] train_sft.py:596 - step 157: {'train/loss': 0.3597869873046875, 'train/grad_norm': 1.4243018627166748, 'train/lr_0': 6.3200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 11:54:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:54:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:52] train_sft.py:596 - step 158: {'train/loss': 0.32928466796875, 'train/grad_norm': 1.154695749282837, 'train/lr_0': 6.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:54:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:54:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:54:56] train_sft.py:596 - step 159: {'train/loss': 0.269744873046875, 'train/grad_norm': 1.5424927473068237, 'train/lr_0': 6.4000000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 11:54:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:54:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:01] train_sft.py:596 - step 160: {'train/loss': 0.322296142578125, 'train/grad_norm': 1.0887091159820557, 'train/lr_0': 6.44e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 11:55:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:55:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:06] train_sft.py:596 - step 161: {'train/loss': 0.2695465087890625, 'train/grad_norm': 1.122143268585205, 'train/lr_0': 6.480000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 11:55:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:55:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:11] train_sft.py:596 - step 162: {'train/loss': 0.26580810546875, 'train/grad_norm': 1.6924231052398682, 'train/lr_0': 6.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 11:55:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:55:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:16] train_sft.py:596 - step 163: {'train/loss': 0.27691650390625, 'train/grad_norm': 2.08827805519104, 'train/lr_0': 6.560000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 11:55:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:55:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:21] train_sft.py:596 - step 164: {'train/loss': 0.305419921875, 'train/grad_norm': 1.439375638961792, 'train/lr_0': 6.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 11:55:21] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:55:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:26] train_sft.py:596 - step 165: {'train/loss': 0.336883544921875, 'train/grad_norm': 1.2932194471359253, 'train/lr_0': 6.640000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:55:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:55:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:31] train_sft.py:596 - step 166: {'train/loss': 0.3087005615234375, 'train/grad_norm': 1.9714480638504028, 'train/lr_0': 6.68e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.14s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.14s/it]
[2025-12-29 11:55:31] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:55:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:37] train_sft.py:596 - step 167: {'train/loss': 0.2777557373046875, 'train/grad_norm': 1.2977312803268433, 'train/lr_0': 6.72e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.40s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.40s/it]
[2025-12-29 11:55:37] timer.py:32 - Timer actor_train end (elapsed: 5.4s)
[2025-12-29 11:55:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:42] train_sft.py:596 - step 168: {'train/loss': 0.275726318359375, 'train/grad_norm': 1.0463361740112305, 'train/lr_0': 6.7600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 11:55:42] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:55:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:47] train_sft.py:596 - step 169: {'train/loss': 0.330535888671875, 'train/grad_norm': 1.719733715057373, 'train/lr_0': 6.8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 11:55:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:55:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:52] train_sft.py:596 - step 170: {'train/loss': 0.2722930908203125, 'train/grad_norm': 1.5138739347457886, 'train/lr_0': 6.840000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]
[2025-12-29 11:55:52] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:55:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:55:57] train_sft.py:596 - step 171: {'train/loss': 0.31878662109375, 'train/grad_norm': 1.648145079612732, 'train/lr_0': 6.880000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 11:55:57] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:55:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:02] train_sft.py:596 - step 172: {'train/loss': 0.317840576171875, 'train/grad_norm': 1.2351298332214355, 'train/lr_0': 6.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 11:56:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:56:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:07] train_sft.py:596 - step 173: {'train/loss': 0.2649993896484375, 'train/grad_norm': 1.5452911853790283, 'train/lr_0': 6.960000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 11:56:07] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:56:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:12] train_sft.py:596 - step 174: {'train/loss': 0.2619781494140625, 'train/grad_norm': 0.9792367219924927, 'train/lr_0': 7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 11:56:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:56:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:17] train_sft.py:596 - step 175: {'train/loss': 0.23626708984375, 'train/grad_norm': 1.5399832725524902, 'train/lr_0': 7.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:56:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:56:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:22] train_sft.py:596 - step 176: {'train/loss': 0.2604827880859375, 'train/grad_norm': 1.258480191230774, 'train/lr_0': 7.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 11:56:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:56:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:27] train_sft.py:596 - step 177: {'train/loss': 0.3321533203125, 'train/grad_norm': 1.3921958208084106, 'train/lr_0': 7.1200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 11:56:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:56:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:32] train_sft.py:596 - step 178: {'train/loss': 0.282073974609375, 'train/grad_norm': 1.2334469556808472, 'train/lr_0': 7.160000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.13s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.13s/it]
[2025-12-29 11:56:32] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:56:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:37] train_sft.py:596 - step 179: {'train/loss': 0.282135009765625, 'train/grad_norm': 1.1085044145584106, 'train/lr_0': 7.2000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 11:56:37] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:56:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:42] train_sft.py:596 - step 180: {'train/loss': 0.2943115234375, 'train/grad_norm': 1.3661466836929321, 'train/lr_0': 7.240000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 11:56:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:56:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:48] train_sft.py:596 - step 181: {'train/loss': 0.3597564697265625, 'train/grad_norm': 1.5061562061309814, 'train/lr_0': 7.280000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 11:56:48] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 11:56:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:53] train_sft.py:596 - step 182: {'train/loss': 0.323883056640625, 'train/grad_norm': 1.7678889036178589, 'train/lr_0': 7.320000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 11:56:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:56:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:56:58] train_sft.py:596 - step 183: {'train/loss': 0.284088134765625, 'train/grad_norm': 1.3340872526168823, 'train/lr_0': 7.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 11:56:58] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:56:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:03] train_sft.py:596 - step 184: {'train/loss': 0.2913665771484375, 'train/grad_norm': 1.5552396774291992, 'train/lr_0': 7.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 11:57:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:57:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:08] train_sft.py:596 - step 185: {'train/loss': 0.2947998046875, 'train/grad_norm': 1.1965327262878418, 'train/lr_0': 7.440000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 11:57:08] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:57:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:13] train_sft.py:596 - step 186: {'train/loss': 0.2553558349609375, 'train/grad_norm': 2.210298776626587, 'train/lr_0': 7.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 11:57:13] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:57:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:19] train_sft.py:596 - step 187: {'train/loss': 0.258453369140625, 'train/grad_norm': 1.0415360927581787, 'train/lr_0': 7.520000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 11:57:19] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:57:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:24] train_sft.py:596 - step 188: {'train/loss': 0.255889892578125, 'train/grad_norm': 2.44968843460083, 'train/lr_0': 7.5600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 11:57:24] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:57:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:29] train_sft.py:596 - step 189: {'train/loss': 0.287322998046875, 'train/grad_norm': 1.4106366634368896, 'train/lr_0': 7.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 11:57:29] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:57:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:34] train_sft.py:596 - step 190: {'train/loss': 0.24391937255859375, 'train/grad_norm': 0.9483813643455505, 'train/lr_0': 7.640000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 11:57:34] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 11:57:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:39] train_sft.py:596 - step 191: {'train/loss': 0.3084716796875, 'train/grad_norm': 1.4895353317260742, 'train/lr_0': 7.680000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 11:57:39] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 11:57:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:44] train_sft.py:596 - step 192: {'train/loss': 0.2937469482421875, 'train/grad_norm': 2.0313236713409424, 'train/lr_0': 7.72e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.37s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.37s/it]
[2025-12-29 11:57:44] timer.py:32 - Timer actor_train end (elapsed: 5.4s)
[2025-12-29 11:57:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:49] train_sft.py:596 - step 193: {'train/loss': 0.318084716796875, 'train/grad_norm': 1.9144779443740845, 'train/lr_0': 7.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 11:57:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:57:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:54] train_sft.py:596 - step 194: {'train/loss': 0.257232666015625, 'train/grad_norm': 1.4510473012924194, 'train/lr_0': 7.8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:57:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:57:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:57:59] train_sft.py:596 - step 195: {'train/loss': 0.315582275390625, 'train/grad_norm': 2.292154550552368, 'train/lr_0': 7.840000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 11:57:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:57:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:04] train_sft.py:596 - step 196: {'train/loss': 0.246856689453125, 'train/grad_norm': 1.1674394607543945, 'train/lr_0': 7.879999999999999e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
[2025-12-29 11:58:04] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 11:58:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:10] train_sft.py:596 - step 197: {'train/loss': 0.281341552734375, 'train/grad_norm': 1.0209299325942993, 'train/lr_0': 7.92e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 11:58:10] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 11:58:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:15] train_sft.py:596 - step 198: {'train/loss': 0.2797088623046875, 'train/grad_norm': 1.3392784595489502, 'train/lr_0': 7.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 11:58:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:58:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:19] train_sft.py:596 - step 199: {'train/loss': 0.27728271484375, 'train/grad_norm': 1.0214883089065552, 'train/lr_0': 8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:58:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:58:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:24] train_sft.py:596 - step 200: {'train/loss': 0.2853546142578125, 'train/grad_norm': 1.5793097019195557, 'train/lr_0': 8.040000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 11:58:24] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 11:58:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:30] train_sft.py:596 - step 201: {'train/loss': 0.240447998046875, 'train/grad_norm': 0.9397693872451782, 'train/lr_0': 8.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 11:58:30] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:58:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:35] train_sft.py:596 - step 202: {'train/loss': 0.3353424072265625, 'train/grad_norm': 1.3301689624786377, 'train/lr_0': 8.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 11:58:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:58:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:41] train_sft.py:596 - step 203: {'train/loss': 0.325531005859375, 'train/grad_norm': 1.2707417011260986, 'train/lr_0': 8.16e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.73s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.73s/it]
[2025-12-29 11:58:41] timer.py:32 - Timer actor_train end (elapsed: 5.7s)
[2025-12-29 11:58:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:46] train_sft.py:596 - step 204: {'train/loss': 0.23944091796875, 'train/grad_norm': 1.3576903343200684, 'train/lr_0': 8.200000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 11:58:46] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 11:58:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:51] train_sft.py:596 - step 205: {'train/loss': 0.2970733642578125, 'train/grad_norm': 2.2628533840179443, 'train/lr_0': 8.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 11:58:51] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:58:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:58:55] train_sft.py:596 - step 206: {'train/loss': 0.220367431640625, 'train/grad_norm': 1.0774476528167725, 'train/lr_0': 8.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:58:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:58:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:00] train_sft.py:596 - step 207: {'train/loss': 0.304962158203125, 'train/grad_norm': 1.26032292842865, 'train/lr_0': 8.320000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 11:59:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:59:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:05] train_sft.py:596 - step 208: {'train/loss': 0.295135498046875, 'train/grad_norm': 1.6997321844100952, 'train/lr_0': 8.360000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:59:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:59:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:10] train_sft.py:596 - step 209: {'train/loss': 0.3029632568359375, 'train/grad_norm': 1.7095983028411865, 'train/lr_0': 8.400000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:59:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:59:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:15] train_sft.py:596 - step 210: {'train/loss': 0.2813720703125, 'train/grad_norm': 1.5517107248306274, 'train/lr_0': 8.44e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 11:59:15] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:59:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:20] train_sft.py:596 - step 211: {'train/loss': 0.239532470703125, 'train/grad_norm': 1.4099974632263184, 'train/lr_0': 8.480000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 11:59:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:59:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:24] train_sft.py:596 - step 212: {'train/loss': 0.320587158203125, 'train/grad_norm': 1.2960305213928223, 'train/lr_0': 8.520000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 11:59:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:59:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:29] train_sft.py:596 - step 213: {'train/loss': 0.2557525634765625, 'train/grad_norm': 0.9468063712120056, 'train/lr_0': 8.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 11:59:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:59:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:33] train_sft.py:596 - step 214: {'train/loss': 0.267669677734375, 'train/grad_norm': 1.3297021389007568, 'train/lr_0': 8.6e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 11:59:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 11:59:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:38] train_sft.py:596 - step 215: {'train/loss': 0.25128173828125, 'train/grad_norm': 1.017336130142212, 'train/lr_0': 8.64e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 11:59:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:59:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:43] train_sft.py:596 - step 216: {'train/loss': 0.3206787109375, 'train/grad_norm': 2.4957544803619385, 'train/lr_0': 8.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 11:59:43] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 11:59:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:48] train_sft.py:596 - step 217: {'train/loss': 0.251068115234375, 'train/grad_norm': 1.4211324453353882, 'train/lr_0': 8.72e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 11:59:48] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 11:59:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:53] train_sft.py:596 - step 218: {'train/loss': 0.2903594970703125, 'train/grad_norm': 1.0954766273498535, 'train/lr_0': 8.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 11:59:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:59:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 11:59:57] train_sft.py:596 - step 219: {'train/loss': 0.225555419921875, 'train/grad_norm': 1.0435045957565308, 'train/lr_0': 8.8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 11:59:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 11:59:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:02] train_sft.py:596 - step 220: {'train/loss': 0.2166748046875, 'train/grad_norm': 1.6324199438095093, 'train/lr_0': 8.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:00:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:00:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:07] train_sft.py:596 - step 221: {'train/loss': 0.270233154296875, 'train/grad_norm': 1.383210301399231, 'train/lr_0': 8.880000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 12:00:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:00:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:12] train_sft.py:596 - step 222: {'train/loss': 0.27593994140625, 'train/grad_norm': 1.4577760696411133, 'train/lr_0': 8.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:00:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:16] train_sft.py:596 - step 223: {'train/loss': 0.2611846923828125, 'train/grad_norm': 1.5766558647155762, 'train/lr_0': 8.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:00:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:21] train_sft.py:596 - step 224: {'train/loss': 0.2433929443359375, 'train/grad_norm': 1.0922819375991821, 'train/lr_0': 9e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:00:21] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:00:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:26] train_sft.py:596 - step 225: {'train/loss': 0.2248992919921875, 'train/grad_norm': 0.9874981045722961, 'train/lr_0': 9.040000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:00:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:30] train_sft.py:596 - step 226: {'train/loss': 0.25542449951171875, 'train/grad_norm': 1.4926801919937134, 'train/lr_0': 9.080000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:00:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:35] train_sft.py:596 - step 227: {'train/loss': 0.2333221435546875, 'train/grad_norm': 1.001434087753296, 'train/lr_0': 9.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:00:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:40] train_sft.py:596 - step 228: {'train/loss': 0.219512939453125, 'train/grad_norm': 1.0127100944519043, 'train/lr_0': 9.160000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 12:00:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:00:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:45] train_sft.py:596 - step 229: {'train/loss': 0.26715087890625, 'train/grad_norm': 1.3033896684646606, 'train/lr_0': 9.200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:00:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:49] train_sft.py:596 - step 230: {'train/loss': 0.2258758544921875, 'train/grad_norm': 1.0784311294555664, 'train/lr_0': 9.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:00:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:54] train_sft.py:596 - step 231: {'train/loss': 0.1905670166015625, 'train/grad_norm': 1.3483185768127441, 'train/lr_0': 9.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:00:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:00:59] train_sft.py:596 - step 232: {'train/loss': 0.2420196533203125, 'train/grad_norm': 1.7607145309448242, 'train/lr_0': 9.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:00:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:00:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:03] train_sft.py:596 - step 233: {'train/loss': 0.2580718994140625, 'train/grad_norm': 1.7351990938186646, 'train/lr_0': 9.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:01:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:08] train_sft.py:596 - step 234: {'train/loss': 0.25042724609375, 'train/grad_norm': 1.0254415273666382, 'train/lr_0': 9.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:01:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:13] train_sft.py:596 - step 235: {'train/loss': 0.2264556884765625, 'train/grad_norm': 0.9993602633476257, 'train/lr_0': 9.440000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:01:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:17] train_sft.py:596 - step 236: {'train/loss': 0.27414703369140625, 'train/grad_norm': 1.634135365486145, 'train/lr_0': 9.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:01:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:22] train_sft.py:596 - step 237: {'train/loss': 0.22418212890625, 'train/grad_norm': 1.2128502130508423, 'train/lr_0': 9.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:01:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:01:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:27] train_sft.py:596 - step 238: {'train/loss': 0.1972198486328125, 'train/grad_norm': 1.3788915872573853, 'train/lr_0': 9.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:01:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:01:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:31] train_sft.py:596 - step 239: {'train/loss': 0.2195892333984375, 'train/grad_norm': 1.0584063529968262, 'train/lr_0': 9.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:01:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:36] train_sft.py:596 - step 240: {'train/loss': 0.2379913330078125, 'train/grad_norm': 1.1524325609207153, 'train/lr_0': 9.640000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:01:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:41] train_sft.py:596 - step 241: {'train/loss': 0.2533416748046875, 'train/grad_norm': 1.1346282958984375, 'train/lr_0': 9.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:01:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:45] train_sft.py:596 - step 242: {'train/loss': 0.21872711181640625, 'train/grad_norm': 1.2958897352218628, 'train/lr_0': 9.720000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:01:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:01:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:50] train_sft.py:596 - step 243: {'train/loss': 0.30474090576171875, 'train/grad_norm': 1.312320590019226, 'train/lr_0': 9.760000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:01:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:01:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:55] train_sft.py:596 - step 244: {'train/loss': 0.2932281494140625, 'train/grad_norm': 1.3405905961990356, 'train/lr_0': 9.800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:01:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:01:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:01:59] train_sft.py:596 - step 245: {'train/loss': 0.2673797607421875, 'train/grad_norm': 1.8082276582717896, 'train/lr_0': 9.840000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:01:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:04] train_sft.py:596 - step 246: {'train/loss': 0.2416839599609375, 'train/grad_norm': 1.129547119140625, 'train/lr_0': 9.880000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:02:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:09] train_sft.py:596 - step 247: {'train/loss': 0.22100830078125, 'train/grad_norm': 1.0818934440612793, 'train/lr_0': 9.92e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:02:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:13] train_sft.py:596 - step 248: {'train/loss': 0.22198486328125, 'train/grad_norm': 1.1854802370071411, 'train/lr_0': 9.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:02:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:02:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:18] train_sft.py:596 - step 249: {'train/loss': 0.22608184814453125, 'train/grad_norm': 2.316516399383545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:02:18] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:02:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:23] train_sft.py:596 - step 250: {'train/loss': 0.2370452880859375, 'train/grad_norm': 1.170060634613037, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:02:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:02:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:27] train_sft.py:596 - step 251: {'train/loss': 0.2024993896484375, 'train/grad_norm': 1.4654489755630493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:02:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:32] train_sft.py:596 - step 252: {'train/loss': 0.23494720458984375, 'train/grad_norm': 1.6493009328842163, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:02:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:37] train_sft.py:596 - step 253: {'train/loss': 0.22083282470703125, 'train/grad_norm': 1.2926626205444336, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:02:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:41] train_sft.py:596 - step 254: {'train/loss': 0.2193603515625, 'train/grad_norm': 1.1375763416290283, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:02:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:02:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:46] train_sft.py:596 - step 255: {'train/loss': 0.2463531494140625, 'train/grad_norm': 0.9995035529136658, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:02:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:51] train_sft.py:596 - step 256: {'train/loss': 0.192413330078125, 'train/grad_norm': 0.9770371317863464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:02:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:02:55] train_sft.py:596 - step 257: {'train/loss': 0.24930191040039062, 'train/grad_norm': 1.686118483543396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:02:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:02:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:00] train_sft.py:596 - step 258: {'train/loss': 0.2305908203125, 'train/grad_norm': 1.321516513824463, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:03:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:03:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:05] train_sft.py:596 - step 259: {'train/loss': 0.2645111083984375, 'train/grad_norm': 0.9359151721000671, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:03:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:09] train_sft.py:596 - step 260: {'train/loss': 0.1916351318359375, 'train/grad_norm': 1.1347287893295288, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:03:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:14] train_sft.py:596 - step 261: {'train/loss': 0.22887420654296875, 'train/grad_norm': 1.739741325378418, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:03:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:03:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:19] train_sft.py:596 - step 262: {'train/loss': 0.20897674560546875, 'train/grad_norm': 1.0193800926208496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:03:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:23] train_sft.py:596 - step 263: {'train/loss': 0.26390838623046875, 'train/grad_norm': 1.7077990770339966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:03:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:28] train_sft.py:596 - step 264: {'train/loss': 0.26416015625, 'train/grad_norm': 0.9006606936454773, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:03:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:33] train_sft.py:596 - step 265: {'train/loss': 0.19183349609375, 'train/grad_norm': 1.1041663885116577, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:03:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:37] train_sft.py:596 - step 266: {'train/loss': 0.23535919189453125, 'train/grad_norm': 1.4614710807800293, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:03:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:42] train_sft.py:596 - step 267: {'train/loss': 0.213775634765625, 'train/grad_norm': 1.0314223766326904, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:03:42] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:03:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:47] train_sft.py:596 - step 268: {'train/loss': 0.1956329345703125, 'train/grad_norm': 0.9528923034667969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:03:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:03:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:51] train_sft.py:596 - step 269: {'train/loss': 0.1983184814453125, 'train/grad_norm': 0.722762942314148, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:03:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:03:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:03:56] train_sft.py:596 - step 270: {'train/loss': 0.2269287109375, 'train/grad_norm': 1.0157651901245117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:03:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:03:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:01] train_sft.py:596 - step 271: {'train/loss': 0.19907379150390625, 'train/grad_norm': 1.04185152053833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:04:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:05] train_sft.py:596 - step 272: {'train/loss': 0.21829986572265625, 'train/grad_norm': 1.3159799575805664, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:04:05] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:04:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:10] train_sft.py:596 - step 273: {'train/loss': 0.18581390380859375, 'train/grad_norm': 0.8893139362335205, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:04:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:15] train_sft.py:596 - step 274: {'train/loss': 0.1905975341796875, 'train/grad_norm': 0.820588231086731, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:04:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:19] train_sft.py:596 - step 275: {'train/loss': 0.231536865234375, 'train/grad_norm': 1.1148761510849, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:04:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:24] train_sft.py:596 - step 276: {'train/loss': 0.2119598388671875, 'train/grad_norm': 1.0582491159439087, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:04:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:04:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:29] train_sft.py:596 - step 277: {'train/loss': 0.22333526611328125, 'train/grad_norm': 0.9532087445259094, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:04:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:33] train_sft.py:596 - step 278: {'train/loss': 0.2615509033203125, 'train/grad_norm': 1.0635454654693604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:04:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:38] train_sft.py:596 - step 279: {'train/loss': 0.19976806640625, 'train/grad_norm': 1.0867406129837036, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:04:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:43] train_sft.py:596 - step 280: {'train/loss': 0.2388916015625, 'train/grad_norm': 1.268034815788269, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:04:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:47] train_sft.py:596 - step 281: {'train/loss': 0.21532440185546875, 'train/grad_norm': 1.2627934217453003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:04:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:52] train_sft.py:596 - step 282: {'train/loss': 0.224090576171875, 'train/grad_norm': 1.1290000677108765, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:04:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:04:57] train_sft.py:596 - step 283: {'train/loss': 0.20192718505859375, 'train/grad_norm': 1.4748892784118652, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:04:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:04:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:01] train_sft.py:596 - step 284: {'train/loss': 0.205230712890625, 'train/grad_norm': 0.9758794903755188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:05:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:06] train_sft.py:596 - step 285: {'train/loss': 0.211212158203125, 'train/grad_norm': 1.190240502357483, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:05:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:11] train_sft.py:596 - step 286: {'train/loss': 0.20746612548828125, 'train/grad_norm': 1.3707821369171143, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:05:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:05:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:15] train_sft.py:596 - step 287: {'train/loss': 0.1815643310546875, 'train/grad_norm': 0.8754623532295227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:05:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:20] train_sft.py:596 - step 288: {'train/loss': 0.19499969482421875, 'train/grad_norm': 0.9928982853889465, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:05:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:24] train_sft.py:596 - step 289: {'train/loss': 0.2129364013671875, 'train/grad_norm': 1.1512428522109985, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:05:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:05:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:29] train_sft.py:596 - step 290: {'train/loss': 0.26020050048828125, 'train/grad_norm': 1.2053190469741821, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:05:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:34] train_sft.py:596 - step 291: {'train/loss': 0.2136993408203125, 'train/grad_norm': 1.3435072898864746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:05:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:05:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:38] train_sft.py:596 - step 292: {'train/loss': 0.219451904296875, 'train/grad_norm': 1.3897241353988647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:05:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:43] train_sft.py:596 - step 293: {'train/loss': 0.2744140625, 'train/grad_norm': 1.444945216178894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:05:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:48] train_sft.py:596 - step 294: {'train/loss': 0.21640777587890625, 'train/grad_norm': 1.3133100271224976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:05:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:05:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:52] train_sft.py:596 - step 295: {'train/loss': 0.28692626953125, 'train/grad_norm': 1.6392115354537964, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:05:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:05:57] train_sft.py:596 - step 296: {'train/loss': 0.2044525146484375, 'train/grad_norm': 0.8222843408584595, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:05:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:05:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:02] train_sft.py:596 - step 297: {'train/loss': 0.18978118896484375, 'train/grad_norm': 1.1116902828216553, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:06:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:06] train_sft.py:596 - step 298: {'train/loss': 0.22180938720703125, 'train/grad_norm': 0.7508429884910583, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:06:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:06:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:11] train_sft.py:596 - step 299: {'train/loss': 0.25499725341796875, 'train/grad_norm': 1.2593623399734497, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:06:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:15] train_sft.py:596 - step 300: {'train/loss': 0.1977691650390625, 'train/grad_norm': 1.6325476169586182, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:06:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:20] train_sft.py:596 - step 301: {'train/loss': 0.230438232421875, 'train/grad_norm': 0.8735982179641724, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:06:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:06:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:25] train_sft.py:596 - step 302: {'train/loss': 0.235687255859375, 'train/grad_norm': 0.7882832884788513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:06:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:29] train_sft.py:596 - step 303: {'train/loss': 0.2541961669921875, 'train/grad_norm': 1.1244741678237915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:06:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:06:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:34] train_sft.py:596 - step 304: {'train/loss': 0.2479248046875, 'train/grad_norm': 1.3950847387313843, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:06:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:39] train_sft.py:596 - step 305: {'train/loss': 0.298553466796875, 'train/grad_norm': 1.1158971786499023, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:06:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:43] train_sft.py:596 - step 306: {'train/loss': 0.20383071899414062, 'train/grad_norm': 1.108493447303772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 12:06:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:06:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:48] train_sft.py:596 - step 307: {'train/loss': 0.2299957275390625, 'train/grad_norm': 2.343999147415161, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:06:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:53] train_sft.py:596 - step 308: {'train/loss': 0.2055816650390625, 'train/grad_norm': 1.1631073951721191, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:06:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:06:57] train_sft.py:596 - step 309: {'train/loss': 0.2439422607421875, 'train/grad_norm': 2.3956568241119385, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:06:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:06:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:02] train_sft.py:596 - step 310: {'train/loss': 0.23172760009765625, 'train/grad_norm': 1.1125246286392212, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:07:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:07:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:07] train_sft.py:596 - step 311: {'train/loss': 0.20326995849609375, 'train/grad_norm': 0.7578198313713074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:07:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:11] train_sft.py:596 - step 312: {'train/loss': 0.2356719970703125, 'train/grad_norm': 0.8080136775970459, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:07:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:16] train_sft.py:596 - step 313: {'train/loss': 0.2208709716796875, 'train/grad_norm': 1.8381850719451904, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:07:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:21] train_sft.py:596 - step 314: {'train/loss': 0.1995391845703125, 'train/grad_norm': 0.889134407043457, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 12:07:21] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:07:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:25] train_sft.py:596 - step 315: {'train/loss': 0.20562744140625, 'train/grad_norm': 0.8400381207466125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:07:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:30] train_sft.py:596 - step 316: {'train/loss': 0.231109619140625, 'train/grad_norm': 0.8611395955085754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:07:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:07:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:35] train_sft.py:596 - step 317: {'train/loss': 0.2266387939453125, 'train/grad_norm': 0.8951762914657593, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 12:07:35] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:07:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:39] train_sft.py:596 - step 318: {'train/loss': 0.2577056884765625, 'train/grad_norm': 0.7069792151451111, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:07:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:44] train_sft.py:596 - step 319: {'train/loss': 0.22295379638671875, 'train/grad_norm': 1.2249627113342285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:07:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:07:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:49] train_sft.py:596 - step 320: {'train/loss': 0.21544647216796875, 'train/grad_norm': 1.0781844854354858, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:07:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:07:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:54] train_sft.py:596 - step 321: {'train/loss': 0.21749114990234375, 'train/grad_norm': 0.9991551041603088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:07:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:07:59] train_sft.py:596 - step 322: {'train/loss': 0.2462615966796875, 'train/grad_norm': 1.302182912826538, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:07:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:07:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:04] train_sft.py:596 - step 323: {'train/loss': 0.23619842529296875, 'train/grad_norm': 1.4053243398666382, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:08:04] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:08:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:08] train_sft.py:596 - step 324: {'train/loss': 0.18340301513671875, 'train/grad_norm': 1.1882151365280151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:08:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:13] train_sft.py:596 - step 325: {'train/loss': 0.178741455078125, 'train/grad_norm': 0.9666219353675842, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:08:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:18] train_sft.py:596 - step 326: {'train/loss': 0.216705322265625, 'train/grad_norm': 1.596535325050354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:08:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:22] train_sft.py:596 - step 327: {'train/loss': 0.2576751708984375, 'train/grad_norm': 1.1698921918869019, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:08:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:08:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:27] train_sft.py:596 - step 328: {'train/loss': 0.240570068359375, 'train/grad_norm': 1.2956156730651855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:08:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:32] train_sft.py:596 - step 329: {'train/loss': 0.28548431396484375, 'train/grad_norm': 1.2254793643951416, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:08:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:37] train_sft.py:596 - step 330: {'train/loss': 0.21198272705078125, 'train/grad_norm': 1.3000705242156982, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:08:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:08:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:41] train_sft.py:596 - step 331: {'train/loss': 0.2701416015625, 'train/grad_norm': 1.290389060974121, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:08:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:08:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:46] train_sft.py:596 - step 332: {'train/loss': 0.182464599609375, 'train/grad_norm': 0.8423604965209961, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:08:46] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:08:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:51] train_sft.py:596 - step 333: {'train/loss': 0.2125244140625, 'train/grad_norm': 1.1646525859832764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:08:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:08:56] train_sft.py:596 - step 334: {'train/loss': 0.2569580078125, 'train/grad_norm': 1.3918532133102417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:08:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:08:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:00] train_sft.py:596 - step 335: {'train/loss': 0.21909332275390625, 'train/grad_norm': 1.3409466743469238, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:09:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:09:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:05] train_sft.py:596 - step 336: {'train/loss': 0.2248077392578125, 'train/grad_norm': 1.2162529230117798, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:09:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:09:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:10] train_sft.py:596 - step 337: {'train/loss': 0.2001953125, 'train/grad_norm': 0.8468009233474731, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:09:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:09:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:14] train_sft.py:596 - step 338: {'train/loss': 0.19100189208984375, 'train/grad_norm': 1.0672805309295654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:09:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:09:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:19] train_sft.py:596 - step 339: {'train/loss': 0.20499420166015625, 'train/grad_norm': 0.7283931374549866, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 12:09:19] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:09:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:25] train_sft.py:596 - step 340: {'train/loss': 0.2367706298828125, 'train/grad_norm': 1.010406732559204, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 12:09:25] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:09:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:29] train_sft.py:596 - step 341: {'train/loss': 0.25553131103515625, 'train/grad_norm': 0.9448493719100952, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:09:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:09:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:35] train_sft.py:596 - step 342: {'train/loss': 0.20119476318359375, 'train/grad_norm': 0.929786205291748, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 12:09:35] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:09:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:40] train_sft.py:596 - step 343: {'train/loss': 0.21656036376953125, 'train/grad_norm': 0.7415600419044495, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 12:09:40] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:09:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:45] train_sft.py:596 - step 344: {'train/loss': 0.2399444580078125, 'train/grad_norm': 1.8170799016952515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:09:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:09:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:50] train_sft.py:596 - step 345: {'train/loss': 0.203216552734375, 'train/grad_norm': 1.355259656906128, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 12:09:50] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:09:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:54] train_sft.py:596 - step 346: {'train/loss': 0.22643280029296875, 'train/grad_norm': 1.2578027248382568, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 12:09:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:09:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:09:59] train_sft.py:596 - step 347: {'train/loss': 0.199798583984375, 'train/grad_norm': 0.8655616044998169, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:09:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:09:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:04] train_sft.py:596 - step 348: {'train/loss': 0.2138519287109375, 'train/grad_norm': 1.4353361129760742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 12:10:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:10:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:09] train_sft.py:596 - step 349: {'train/loss': 0.2186126708984375, 'train/grad_norm': 1.253239393234253, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:10:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:10:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:14] train_sft.py:596 - step 350: {'train/loss': 0.21649169921875, 'train/grad_norm': 0.9901922941207886, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 12:10:14] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:10:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:19] train_sft.py:596 - step 351: {'train/loss': 0.1975860595703125, 'train/grad_norm': 1.0888630151748657, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:10:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:10:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:24] train_sft.py:596 - step 352: {'train/loss': 0.263031005859375, 'train/grad_norm': 1.4207223653793335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:10:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:10:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:29] train_sft.py:596 - step 353: {'train/loss': 0.2263946533203125, 'train/grad_norm': 0.9923866987228394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 12:10:29] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:10:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:34] train_sft.py:596 - step 354: {'train/loss': 0.22339630126953125, 'train/grad_norm': 0.8525013327598572, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:10:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:10:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:39] train_sft.py:596 - step 355: {'train/loss': 0.19842910766601562, 'train/grad_norm': 1.5526413917541504, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 12:10:39] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:10:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:44] train_sft.py:596 - step 356: {'train/loss': 0.20164871215820312, 'train/grad_norm': 1.242307424545288, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 12:10:44] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:10:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:49] train_sft.py:596 - step 357: {'train/loss': 0.21822357177734375, 'train/grad_norm': 1.162070870399475, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 12:10:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:10:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:53] train_sft.py:596 - step 358: {'train/loss': 0.2165985107421875, 'train/grad_norm': 0.6995159387588501, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:10:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:10:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:10:58] train_sft.py:596 - step 359: {'train/loss': 0.193603515625, 'train/grad_norm': 0.9887024164199829, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 12:10:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:10:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:03] train_sft.py:596 - step 360: {'train/loss': 0.18109130859375, 'train/grad_norm': 0.5785213708877563, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 12:11:03] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:11:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:09] train_sft.py:596 - step 361: {'train/loss': 0.209625244140625, 'train/grad_norm': 0.8899946808815002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 12:11:09] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:11:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:14] train_sft.py:596 - step 362: {'train/loss': 0.2572174072265625, 'train/grad_norm': 0.9018158912658691, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 12:11:14] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:11:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:19] train_sft.py:596 - step 363: {'train/loss': 0.194793701171875, 'train/grad_norm': 0.9543343186378479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 12:11:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:11:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:24] train_sft.py:596 - step 364: {'train/loss': 0.269775390625, 'train/grad_norm': 1.2043415307998657, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.10s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.10s/it]
[2025-12-29 12:11:24] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:11:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:29] train_sft.py:596 - step 365: {'train/loss': 0.205902099609375, 'train/grad_norm': 1.2621463537216187, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 12:11:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:11:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:35] train_sft.py:596 - step 366: {'train/loss': 0.2338409423828125, 'train/grad_norm': 1.1459556818008423, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.48s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.48s/it]
[2025-12-29 12:11:35] timer.py:32 - Timer actor_train end (elapsed: 5.5s)
[2025-12-29 12:11:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:40] train_sft.py:596 - step 367: {'train/loss': 0.2245025634765625, 'train/grad_norm': 0.692000687122345, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 12:11:40] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:11:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:45] train_sft.py:596 - step 368: {'train/loss': 0.24623870849609375, 'train/grad_norm': 2.0260121822357178, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:11:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:11:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:49] train_sft.py:596 - step 369: {'train/loss': 0.18511962890625, 'train/grad_norm': 0.9118410348892212, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:11:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:11:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:54] train_sft.py:596 - step 370: {'train/loss': 0.21988677978515625, 'train/grad_norm': 1.3348724842071533, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:11:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:11:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:11:59] train_sft.py:596 - step 371: {'train/loss': 0.22601318359375, 'train/grad_norm': 1.2352524995803833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:11:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:11:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:04] train_sft.py:596 - step 372: {'train/loss': 0.2203216552734375, 'train/grad_norm': 1.2761592864990234, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 12:12:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:12:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:09] train_sft.py:596 - step 373: {'train/loss': 0.19594573974609375, 'train/grad_norm': 1.0010851621627808, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
[2025-12-29 12:12:09] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:12:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:14] train_sft.py:596 - step 374: {'train/loss': 0.1920166015625, 'train/grad_norm': 0.6770466566085815, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:12:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:12:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:19] train_sft.py:596 - step 375: {'train/loss': 0.2155609130859375, 'train/grad_norm': 1.2133184671401978, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:12:19] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:12:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:24] train_sft.py:596 - step 376: {'train/loss': 0.18170166015625, 'train/grad_norm': 0.6453149914741516, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:12:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:12:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:29] train_sft.py:596 - step 377: {'train/loss': 0.20804595947265625, 'train/grad_norm': 1.1157788038253784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.32s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.32s/it]
[2025-12-29 12:12:29] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 12:12:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:34] train_sft.py:596 - step 378: {'train/loss': 0.2884063720703125, 'train/grad_norm': 1.1780883073806763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 12:12:34] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:12:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:39] train_sft.py:596 - step 379: {'train/loss': 0.21803665161132812, 'train/grad_norm': 1.0129077434539795, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 12:12:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:12:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:44] train_sft.py:596 - step 380: {'train/loss': 0.1917572021484375, 'train/grad_norm': 0.9989028573036194, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:12:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:12:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:48] train_sft.py:596 - step 381: {'train/loss': 0.214935302734375, 'train/grad_norm': 1.8088572025299072, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:12:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:12:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:53] train_sft.py:596 - step 382: {'train/loss': 0.1917572021484375, 'train/grad_norm': 0.8618941307067871, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:12:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:12:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:12:58] train_sft.py:596 - step 383: {'train/loss': 0.1992645263671875, 'train/grad_norm': 1.2240530252456665, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:12:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:12:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:03] train_sft.py:596 - step 384: {'train/loss': 0.1767730712890625, 'train/grad_norm': 0.941594660282135, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 12:13:03] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:13:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:08] train_sft.py:596 - step 385: {'train/loss': 0.22010040283203125, 'train/grad_norm': 0.9317080974578857, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:13:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:13:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:13] train_sft.py:596 - step 386: {'train/loss': 0.2066192626953125, 'train/grad_norm': 0.8975040912628174, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.38s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.38s/it]
[2025-12-29 12:13:13] timer.py:32 - Timer actor_train end (elapsed: 5.4s)
[2025-12-29 12:13:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:19] train_sft.py:596 - step 387: {'train/loss': 0.2204437255859375, 'train/grad_norm': 1.0287684202194214, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 12:13:19] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:13:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:23] train_sft.py:596 - step 388: {'train/loss': 0.207733154296875, 'train/grad_norm': 1.421772837638855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 12:13:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:13:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:28] train_sft.py:596 - step 389: {'train/loss': 0.2041015625, 'train/grad_norm': 1.294372797012329, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:13:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:13:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:33] train_sft.py:596 - step 390: {'train/loss': 0.19089508056640625, 'train/grad_norm': 0.8673694729804993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:13:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:13:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:38] train_sft.py:596 - step 391: {'train/loss': 0.2399139404296875, 'train/grad_norm': 1.0017352104187012, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:13:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:13:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:42] train_sft.py:596 - step 392: {'train/loss': 0.2195281982421875, 'train/grad_norm': 0.8503662347793579, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:13:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:13:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:48] train_sft.py:596 - step 393: {'train/loss': 0.20316314697265625, 'train/grad_norm': 0.8095157742500305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 12:13:48] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:13:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:52] train_sft.py:596 - step 394: {'train/loss': 0.20946502685546875, 'train/grad_norm': 0.9203505516052246, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:13:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:13:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:13:58] train_sft.py:596 - step 395: {'train/loss': 0.20611572265625, 'train/grad_norm': 1.0524252653121948, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 12:13:58] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:13:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:02] train_sft.py:596 - step 396: {'train/loss': 0.2295379638671875, 'train/grad_norm': 1.178718090057373, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:14:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:14:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:07] train_sft.py:596 - step 397: {'train/loss': 0.2119293212890625, 'train/grad_norm': 0.9369759559631348, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:14:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:14:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:12] train_sft.py:596 - step 398: {'train/loss': 0.252471923828125, 'train/grad_norm': 1.250961422920227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
[2025-12-29 12:14:12] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:14:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:17] train_sft.py:596 - step 399: {'train/loss': 0.2171478271484375, 'train/grad_norm': 0.7372480034828186, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:14:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:14:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:22] train_sft.py:596 - step 400: {'train/loss': 0.2285003662109375, 'train/grad_norm': 0.7825399041175842, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:14:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:14:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:27] train_sft.py:596 - step 401: {'train/loss': 0.2345428466796875, 'train/grad_norm': 0.8343193531036377, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:14:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:14:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:32] train_sft.py:596 - step 402: {'train/loss': 0.250213623046875, 'train/grad_norm': 1.1650968790054321, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:14:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:14:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:36] train_sft.py:596 - step 403: {'train/loss': 0.1842193603515625, 'train/grad_norm': 0.7338670492172241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:14:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:14:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:41] train_sft.py:596 - step 404: {'train/loss': 0.2029571533203125, 'train/grad_norm': 0.701593816280365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:14:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:14:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:46] train_sft.py:596 - step 405: {'train/loss': 0.242706298828125, 'train/grad_norm': 0.9417787194252014, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:14:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:14:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:51] train_sft.py:596 - step 406: {'train/loss': 0.24652099609375, 'train/grad_norm': 1.5135111808776855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:14:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:14:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:14:55] train_sft.py:596 - step 407: {'train/loss': 0.200897216796875, 'train/grad_norm': 1.0725923776626587, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:14:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:14:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:00] train_sft.py:596 - step 408: {'train/loss': 0.207183837890625, 'train/grad_norm': 1.359309434890747, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:15:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:15:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:05] train_sft.py:596 - step 409: {'train/loss': 0.24794769287109375, 'train/grad_norm': 1.1486232280731201, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:15:05] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:15:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:09] train_sft.py:596 - step 410: {'train/loss': 0.22286605834960938, 'train/grad_norm': 0.8566429615020752, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:15:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:14] train_sft.py:596 - step 411: {'train/loss': 0.20345687866210938, 'train/grad_norm': 1.0167120695114136, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:15:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:19] train_sft.py:596 - step 412: {'train/loss': 0.20599365234375, 'train/grad_norm': 0.9420050978660583, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:15:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:15:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:24] train_sft.py:596 - step 413: {'train/loss': 0.2180938720703125, 'train/grad_norm': 1.0417418479919434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:15:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:15:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:28] train_sft.py:596 - step 414: {'train/loss': 0.21422576904296875, 'train/grad_norm': 1.120631217956543, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:15:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:15:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:33] train_sft.py:596 - step 415: {'train/loss': 0.201263427734375, 'train/grad_norm': 0.7018970847129822, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:15:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:38] train_sft.py:596 - step 416: {'train/loss': 0.2397918701171875, 'train/grad_norm': 1.6574337482452393, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:15:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:42] train_sft.py:596 - step 417: {'train/loss': 0.21923065185546875, 'train/grad_norm': 1.0682607889175415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:15:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:15:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:47] train_sft.py:596 - step 418: {'train/loss': 0.18798828125, 'train/grad_norm': 0.7670508623123169, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:15:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:52] train_sft.py:596 - step 419: {'train/loss': 0.19241714477539062, 'train/grad_norm': 0.8501628041267395, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:15:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:15:56] train_sft.py:596 - step 420: {'train/loss': 0.19572830200195312, 'train/grad_norm': 0.9337818622589111, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:15:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:15:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:01] train_sft.py:596 - step 421: {'train/loss': 0.22414398193359375, 'train/grad_norm': 1.539846420288086, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:16:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:16:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:05] train_sft.py:596 - step 422: {'train/loss': 0.1645660400390625, 'train/grad_norm': 0.7326103448867798, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:16:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:10] train_sft.py:596 - step 423: {'train/loss': 0.2188568115234375, 'train/grad_norm': 0.7759798169136047, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:16:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:15] train_sft.py:596 - step 424: {'train/loss': 0.17808914184570312, 'train/grad_norm': 0.7426679134368896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:16:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:20] train_sft.py:596 - step 425: {'train/loss': 0.21532440185546875, 'train/grad_norm': 2.377060890197754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:16:20] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:16:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:24] train_sft.py:596 - step 426: {'train/loss': 0.2219390869140625, 'train/grad_norm': 1.0231162309646606, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:16:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:16:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:29] train_sft.py:596 - step 427: {'train/loss': 0.19176864624023438, 'train/grad_norm': 1.0154064893722534, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:16:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:34] train_sft.py:596 - step 428: {'train/loss': 0.207366943359375, 'train/grad_norm': 1.0775797367095947, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:16:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:38] train_sft.py:596 - step 429: {'train/loss': 0.17807769775390625, 'train/grad_norm': 0.6759466528892517, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:16:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:43] train_sft.py:596 - step 430: {'train/loss': 0.21023178100585938, 'train/grad_norm': 0.7548009157180786, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:16:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:48] train_sft.py:596 - step 431: {'train/loss': 0.20746898651123047, 'train/grad_norm': 1.3826655149459839, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:16:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:16:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:52] train_sft.py:596 - step 432: {'train/loss': 0.217529296875, 'train/grad_norm': 0.8621050119400024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:16:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:16:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:16:57] train_sft.py:596 - step 433: {'train/loss': 0.2295074462890625, 'train/grad_norm': 0.9801973104476929, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:16:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:16:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:02] train_sft.py:596 - step 434: {'train/loss': 0.20550537109375, 'train/grad_norm': 0.6625545024871826, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:17:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:06] train_sft.py:596 - step 435: {'train/loss': 0.22829437255859375, 'train/grad_norm': 0.8792963624000549, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:17:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:11] train_sft.py:596 - step 436: {'train/loss': 0.17067337036132812, 'train/grad_norm': 0.6928104162216187, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:17:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:16] train_sft.py:596 - step 437: {'train/loss': 0.18506240844726562, 'train/grad_norm': 0.7527827620506287, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:17:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:20] train_sft.py:596 - step 438: {'train/loss': 0.19121551513671875, 'train/grad_norm': 0.9579184055328369, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:17:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:25] train_sft.py:596 - step 439: {'train/loss': 0.21332931518554688, 'train/grad_norm': 0.8985626101493835, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:17:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:17:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:29] train_sft.py:596 - step 440: {'train/loss': 0.24680328369140625, 'train/grad_norm': 1.1281596422195435, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:17:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:34] train_sft.py:596 - step 441: {'train/loss': 0.20136260986328125, 'train/grad_norm': 0.8383771181106567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:17:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:17:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:39] train_sft.py:596 - step 442: {'train/loss': 0.20265960693359375, 'train/grad_norm': 0.8595110774040222, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:17:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:43] train_sft.py:596 - step 443: {'train/loss': 0.18243408203125, 'train/grad_norm': 0.7386904954910278, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:17:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:48] train_sft.py:596 - step 444: {'train/loss': 0.2158050537109375, 'train/grad_norm': 0.8408291935920715, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:17:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:53] train_sft.py:596 - step 445: {'train/loss': 0.249237060546875, 'train/grad_norm': 0.9479732513427734, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:17:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:17:57] train_sft.py:596 - step 446: {'train/loss': 0.2386627197265625, 'train/grad_norm': 1.264147162437439, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:17:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:17:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:02] train_sft.py:596 - step 447: {'train/loss': 0.21645355224609375, 'train/grad_norm': 1.5062575340270996, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:18:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:18:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:07] train_sft.py:596 - step 448: {'train/loss': 0.22412109375, 'train/grad_norm': 1.0133494138717651, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:18:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:11] train_sft.py:596 - step 449: {'train/loss': 0.18708038330078125, 'train/grad_norm': 1.2874712944030762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:18:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:16] train_sft.py:596 - step 450: {'train/loss': 0.1629486083984375, 'train/grad_norm': 0.7287649512290955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:18:16] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:18:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:21] train_sft.py:596 - step 451: {'train/loss': 0.15076065063476562, 'train/grad_norm': 0.7989925146102905, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:18:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:25] train_sft.py:596 - step 452: {'train/loss': 0.15867996215820312, 'train/grad_norm': 0.9158186912536621, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:18:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:30] train_sft.py:596 - step 453: {'train/loss': 0.1497955322265625, 'train/grad_norm': 0.6729009747505188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:18:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:35] train_sft.py:596 - step 454: {'train/loss': 0.15700531005859375, 'train/grad_norm': 1.0500162839889526, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:18:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:39] train_sft.py:596 - step 455: {'train/loss': 0.169342041015625, 'train/grad_norm': 1.164010763168335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:18:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:44] train_sft.py:596 - step 456: {'train/loss': 0.16100311279296875, 'train/grad_norm': 1.3234766721725464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:18:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:48] train_sft.py:596 - step 457: {'train/loss': 0.14697647094726562, 'train/grad_norm': 0.7814332246780396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:18:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:53] train_sft.py:596 - step 458: {'train/loss': 0.1454296112060547, 'train/grad_norm': 1.165664792060852, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:18:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:18:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:18:58] train_sft.py:596 - step 459: {'train/loss': 0.13910293579101562, 'train/grad_norm': 0.7595565915107727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:18:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:18:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:02] train_sft.py:596 - step 460: {'train/loss': 0.178131103515625, 'train/grad_norm': 1.1629793643951416, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:19:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:07] train_sft.py:596 - step 461: {'train/loss': 0.14641571044921875, 'train/grad_norm': 1.1272258758544922, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:19:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:12] train_sft.py:596 - step 462: {'train/loss': 0.13568687438964844, 'train/grad_norm': 0.8083186745643616, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:19:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:16] train_sft.py:596 - step 463: {'train/loss': 0.12187576293945312, 'train/grad_norm': 0.9815583825111389, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:19:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:21] train_sft.py:596 - step 464: {'train/loss': 0.1367950439453125, 'train/grad_norm': 1.1414777040481567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:19:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:26] train_sft.py:596 - step 465: {'train/loss': 0.147308349609375, 'train/grad_norm': 0.8628993630409241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:19:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:30] train_sft.py:596 - step 466: {'train/loss': 0.14083099365234375, 'train/grad_norm': 0.8864404559135437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:19:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:35] train_sft.py:596 - step 467: {'train/loss': 0.1301431655883789, 'train/grad_norm': 0.7154260873794556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:19:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:40] train_sft.py:596 - step 468: {'train/loss': 0.12569713592529297, 'train/grad_norm': 0.6042193174362183, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:19:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:19:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:44] train_sft.py:596 - step 469: {'train/loss': 0.119659423828125, 'train/grad_norm': 1.6501274108886719, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:19:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:49] train_sft.py:596 - step 470: {'train/loss': 0.12799930572509766, 'train/grad_norm': 0.7954491972923279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:19:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:54] train_sft.py:596 - step 471: {'train/loss': 0.12362480163574219, 'train/grad_norm': 1.1968597173690796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:19:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:19:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:19:58] train_sft.py:596 - step 472: {'train/loss': 0.12970733642578125, 'train/grad_norm': 0.7284862399101257, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:19:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:19:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:03] train_sft.py:596 - step 473: {'train/loss': 0.13690948486328125, 'train/grad_norm': 0.9764233827590942, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:20:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:07] train_sft.py:596 - step 474: {'train/loss': 0.14423370361328125, 'train/grad_norm': 0.8605054616928101, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:20:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:12] train_sft.py:596 - step 475: {'train/loss': 0.12941741943359375, 'train/grad_norm': 0.7738553881645203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:20:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:17] train_sft.py:596 - step 476: {'train/loss': 0.12617874145507812, 'train/grad_norm': 0.9848814010620117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:20:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:20:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:21] train_sft.py:596 - step 477: {'train/loss': 0.1280384063720703, 'train/grad_norm': 0.8140234351158142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:20:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:26] train_sft.py:596 - step 478: {'train/loss': 0.14365386962890625, 'train/grad_norm': 0.6637463569641113, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:20:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:20:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:31] train_sft.py:596 - step 479: {'train/loss': 0.14600181579589844, 'train/grad_norm': 1.373663306236267, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:20:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:35] train_sft.py:596 - step 480: {'train/loss': 0.14553451538085938, 'train/grad_norm': 1.2613263130187988, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:20:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:20:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:40] train_sft.py:596 - step 481: {'train/loss': 0.14272308349609375, 'train/grad_norm': 0.8119770884513855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:20:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:20:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:45] train_sft.py:596 - step 482: {'train/loss': 0.15184783935546875, 'train/grad_norm': 0.9691662788391113, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:20:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:49] train_sft.py:596 - step 483: {'train/loss': 0.1437530517578125, 'train/grad_norm': 0.876779317855835, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:20:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:20:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:54] train_sft.py:596 - step 484: {'train/loss': 0.14788055419921875, 'train/grad_norm': 0.8176848888397217, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:20:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:20:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:20:59] train_sft.py:596 - step 485: {'train/loss': 0.13646316528320312, 'train/grad_norm': 0.6639990210533142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:20:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:20:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:04] train_sft.py:596 - step 486: {'train/loss': 0.15158653259277344, 'train/grad_norm': 1.4181187152862549, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:21:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:21:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:08] train_sft.py:596 - step 487: {'train/loss': 0.1320953369140625, 'train/grad_norm': 0.942337155342102, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:21:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:13] train_sft.py:596 - step 488: {'train/loss': 0.113525390625, 'train/grad_norm': 0.6338692307472229, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:21:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:18] train_sft.py:596 - step 489: {'train/loss': 0.1215972900390625, 'train/grad_norm': 0.7291020154953003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:21:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:21:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:22] train_sft.py:596 - step 490: {'train/loss': 0.1438770294189453, 'train/grad_norm': 0.9450863599777222, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:21:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:27] train_sft.py:596 - step 491: {'train/loss': 0.15474319458007812, 'train/grad_norm': 0.9308070540428162, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:21:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:32] train_sft.py:596 - step 492: {'train/loss': 0.17092132568359375, 'train/grad_norm': 1.24608314037323, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:21:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:37] train_sft.py:596 - step 493: {'train/loss': 0.1292724609375, 'train/grad_norm': 0.8409422039985657, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:21:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:21:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:41] train_sft.py:596 - step 494: {'train/loss': 0.15892791748046875, 'train/grad_norm': 0.7732368111610413, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:21:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:46] train_sft.py:596 - step 495: {'train/loss': 0.14105749130249023, 'train/grad_norm': 1.34602952003479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:21:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:21:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:51] train_sft.py:596 - step 496: {'train/loss': 0.1427440643310547, 'train/grad_norm': 0.7953029274940491, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:21:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:21:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:21:56] train_sft.py:596 - step 497: {'train/loss': 0.1207571029663086, 'train/grad_norm': 1.3203307390213013, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:21:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:21:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:22:00] train_sft.py:596 - step 498: {'train/loss': 0.17763328552246094, 'train/grad_norm': 1.0824564695358276, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:22:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:22:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:22:05] train_sft.py:596 - step 499: {'train/loss': 0.14629364013671875, 'train/grad_norm': 0.730676531791687, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 12:22:05] timer.py:32 - Timer actor_train end (elapsed: 4.9s)

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:01<02:50,  1.72s/it]
actor_val:   2%|▏         | 2/100 [00:03<02:39,  1.63s/it]
actor_val:   3%|▎         | 3/100 [00:04<02:36,  1.61s/it]
actor_val:   4%|▍         | 4/100 [00:06<02:33,  1.60s/it]
actor_val:   5%|▌         | 5/100 [00:08<02:30,  1.59s/it]
actor_val:   6%|▌         | 6/100 [00:09<02:28,  1.58s/it]
actor_val:   7%|▋         | 7/100 [00:11<02:27,  1.58s/it]
actor_val:   8%|▊         | 8/100 [00:12<02:24,  1.57s/it]
actor_val:   9%|▉         | 9/100 [00:14<02:22,  1.56s/it]
actor_val:  10%|█         | 10/100 [00:15<02:20,  1.56s/it]
actor_val:  11%|█         | 11/100 [00:17<02:19,  1.56s/it]
actor_val:  12%|█▏        | 12/100 [00:19<02:26,  1.66s/it]
actor_val:  13%|█▎        | 13/100 [00:21<02:29,  1.72s/it]
actor_val:  14%|█▍        | 14/100 [00:22<02:24,  1.68s/it]
actor_val:  15%|█▌        | 15/100 [00:24<02:20,  1.65s/it]
actor_val:  16%|█▌        | 16/100 [00:25<02:16,  1.63s/it]
actor_val:  17%|█▋        | 17/100 [00:27<02:13,  1.60s/it]
actor_val:  18%|█▊        | 18/100 [00:28<02:10,  1.59s/it]
actor_val:  19%|█▉        | 19/100 [00:30<02:08,  1.59s/it]
actor_val:  20%|██        | 20/100 [00:32<02:13,  1.67s/it]
actor_val:  21%|██        | 21/100 [00:33<02:09,  1.64s/it]
actor_val:  22%|██▏       | 22/100 [00:35<02:06,  1.62s/it]
actor_val:  23%|██▎       | 23/100 [00:37<02:03,  1.60s/it]
actor_val:  24%|██▍       | 24/100 [00:38<02:00,  1.59s/it]
actor_val:  25%|██▌       | 25/100 [00:40<01:59,  1.59s/it]
actor_val:  26%|██▌       | 26/100 [00:41<01:57,  1.58s/it]
actor_val:  27%|██▋       | 27/100 [00:43<01:55,  1.58s/it]
actor_val:  28%|██▊       | 28/100 [00:44<01:52,  1.57s/it]
actor_val:  29%|██▉       | 29/100 [00:46<01:50,  1.56s/it]
actor_val:  30%|███       | 30/100 [00:48<01:49,  1.56s/it]
actor_val:  31%|███       | 31/100 [00:49<01:48,  1.57s/it]
actor_val:  32%|███▏      | 32/100 [00:51<01:53,  1.67s/it]
actor_val:  33%|███▎      | 33/100 [00:53<01:50,  1.64s/it]
actor_val:  34%|███▍      | 34/100 [00:54<01:46,  1.62s/it]
actor_val:  35%|███▌      | 35/100 [00:56<01:43,  1.60s/it]
actor_val:  36%|███▌      | 36/100 [00:57<01:41,  1.59s/it]
actor_val:  37%|███▋      | 37/100 [00:59<01:39,  1.59s/it]
actor_val:  38%|███▊      | 38/100 [01:01<01:38,  1.59s/it]
actor_val:  39%|███▉      | 39/100 [01:02<01:36,  1.59s/it]
actor_val:  40%|████      | 40/100 [01:04<01:34,  1.57s/it]
actor_val:  41%|████      | 41/100 [01:05<01:33,  1.58s/it]
actor_val:  42%|████▏     | 42/100 [01:07<01:31,  1.59s/it]
actor_val:  43%|████▎     | 43/100 [01:09<01:35,  1.67s/it]
actor_val:  44%|████▍     | 44/100 [01:11<01:36,  1.73s/it]
actor_val:  45%|████▌     | 45/100 [01:12<01:37,  1.78s/it]
actor_val:  46%|████▌     | 46/100 [01:14<01:32,  1.71s/it]
actor_val:  47%|████▋     | 47/100 [01:16<01:34,  1.78s/it]
actor_val:  48%|████▊     | 48/100 [01:18<01:29,  1.72s/it]
actor_val:  49%|████▉     | 49/100 [01:19<01:30,  1.78s/it]
actor_val:  50%|█████     | 50/100 [01:21<01:29,  1.79s/it]
actor_val:  51%|█████     | 51/100 [01:23<01:28,  1.81s/it]
actor_val:  52%|█████▏    | 52/100 [01:25<01:23,  1.73s/it]
actor_val:  53%|█████▎    | 53/100 [01:26<01:19,  1.70s/it]
actor_val:  54%|█████▍    | 54/100 [01:28<01:16,  1.66s/it]
actor_val:  55%|█████▌    | 55/100 [01:29<01:13,  1.63s/it]
actor_val:  56%|█████▌    | 56/100 [01:31<01:10,  1.61s/it]
actor_val:  57%|█████▋    | 57/100 [01:33<01:12,  1.69s/it]
actor_val:  58%|█████▊    | 58/100 [01:35<01:12,  1.72s/it]
actor_val:  59%|█████▉    | 59/100 [01:37<01:13,  1.80s/it]
actor_val:  60%|██████    | 60/100 [01:38<01:09,  1.74s/it]
actor_val:  61%|██████    | 61/100 [01:40<01:11,  1.82s/it]
actor_val:  62%|██████▏   | 62/100 [01:42<01:09,  1.84s/it]
actor_val:  63%|██████▎   | 63/100 [01:44<01:05,  1.76s/it]
actor_val:  64%|██████▍   | 64/100 [01:45<01:01,  1.70s/it]
actor_val:  65%|██████▌   | 65/100 [01:47<00:58,  1.67s/it]
actor_val:  66%|██████▌   | 66/100 [01:49<00:58,  1.72s/it]
actor_val:  67%|██████▋   | 67/100 [01:50<00:55,  1.67s/it]
actor_val:  68%|██████▊   | 68/100 [01:52<00:55,  1.75s/it]
actor_val:  69%|██████▉   | 69/100 [01:54<00:54,  1.77s/it]
actor_val:  70%|███████   | 70/100 [01:56<00:50,  1.70s/it]
actor_val:  71%|███████   | 71/100 [01:57<00:48,  1.66s/it]
actor_val:  72%|███████▏  | 72/100 [01:59<00:48,  1.73s/it]
actor_val:  73%|███████▎  | 73/100 [02:01<00:47,  1.75s/it]
actor_val:  74%|███████▍  | 74/100 [02:03<00:46,  1.79s/it]
actor_val:  75%|███████▌  | 75/100 [02:04<00:42,  1.71s/it]
actor_val:  76%|███████▌  | 76/100 [02:06<00:40,  1.67s/it]
actor_val:  77%|███████▋  | 77/100 [02:07<00:37,  1.63s/it]
actor_val:  78%|███████▊  | 78/100 [02:09<00:37,  1.71s/it]
actor_val:  79%|███████▉  | 79/100 [02:11<00:35,  1.67s/it]
actor_val:  80%|████████  | 80/100 [02:12<00:33,  1.65s/it]
actor_val:  81%|████████  | 81/100 [02:14<00:30,  1.63s/it]
actor_val:  82%|████████▏ | 82/100 [02:16<00:30,  1.71s/it]
actor_val:  83%|████████▎ | 83/100 [02:18<00:29,  1.73s/it]
actor_val:  84%|████████▍ | 84/100 [02:19<00:26,  1.69s/it]
actor_val:  85%|████████▌ | 85/100 [02:21<00:24,  1.65s/it]
actor_val:  86%|████████▌ | 86/100 [02:23<00:24,  1.71s/it]
actor_val:  87%|████████▋ | 87/100 [02:24<00:21,  1.67s/it]
actor_val:  88%|████████▊ | 88/100 [02:26<00:19,  1.64s/it]
actor_val:  89%|████████▉ | 89/100 [02:27<00:17,  1.62s/it]
actor_val:  90%|█████████ | 90/100 [02:29<00:16,  1.60s/it]
actor_val:  91%|█████████ | 91/100 [02:30<00:14,  1.58s/it]
actor_val:  92%|█████████▏| 92/100 [02:32<00:13,  1.68s/it]
actor_val:  93%|█████████▎| 93/100 [02:34<00:11,  1.65s/it]
actor_val:  94%|█████████▍| 94/100 [02:35<00:09,  1.62s/it]
actor_val:  95%|█████████▌| 95/100 [02:37<00:08,  1.68s/it]
actor_val:  96%|█████████▌| 96/100 [02:39<00:07,  1.75s/it]
actor_val:  97%|█████████▋| 97/100 [02:41<00:05,  1.69s/it]
actor_val:  98%|█████████▊| 98/100 [02:43<00:03,  1.75s/it]
actor_val:  99%|█████████▉| 99/100 [02:44<00:01,  1.69s/it]
actor_val: 100%|██████████| 100/100 [02:46<00:00,  1.65s/it]
actor_val: 100%|██████████| 100/100 [02:46<00:00,  1.66s/it]
[2025-12-29 12:24:52] train_sft.py:658 - step 500: {'val/loss': 0.275}
[2025-12-29 12:24:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:24:57] train_sft.py:596 - step 500: {'train/loss': 0.13347625732421875, 'train/grad_norm': 0.7653515934944153, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 12:24:57] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:24:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:02] train_sft.py:596 - step 501: {'train/loss': 0.12097740173339844, 'train/grad_norm': 0.6961597204208374, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 12:25:02] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:25:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:07] train_sft.py:596 - step 502: {'train/loss': 0.14628028869628906, 'train/grad_norm': 1.1352806091308594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 12:25:07] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:25:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:12] train_sft.py:596 - step 503: {'train/loss': 0.14695358276367188, 'train/grad_norm': 0.9821752905845642, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:25:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:25:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:17] train_sft.py:596 - step 504: {'train/loss': 0.151336669921875, 'train/grad_norm': 1.2024645805358887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:25:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:25:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:22] train_sft.py:596 - step 505: {'train/loss': 0.14044189453125, 'train/grad_norm': 0.6544346809387207, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]
[2025-12-29 12:25:22] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 12:25:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:28] train_sft.py:596 - step 506: {'train/loss': 0.1422119140625, 'train/grad_norm': 0.8961553573608398, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:25:28] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:25:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:33] train_sft.py:596 - step 507: {'train/loss': 0.12816393375396729, 'train/grad_norm': 0.8876768946647644, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 12:25:33] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:25:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:38] train_sft.py:596 - step 508: {'train/loss': 0.13583755493164062, 'train/grad_norm': 1.1768198013305664, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 12:25:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:25:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:43] train_sft.py:596 - step 509: {'train/loss': 0.12040901184082031, 'train/grad_norm': 0.6675881743431091, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 12:25:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:25:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:47] train_sft.py:596 - step 510: {'train/loss': 0.14789581298828125, 'train/grad_norm': 0.9782643914222717, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:25:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:25:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:53] train_sft.py:596 - step 511: {'train/loss': 0.1582489013671875, 'train/grad_norm': 1.7517073154449463, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:25:53] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:25:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:25:57] train_sft.py:596 - step 512: {'train/loss': 0.135467529296875, 'train/grad_norm': 0.9520133137702942, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:25:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:25:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:03] train_sft.py:596 - step 513: {'train/loss': 0.12976551055908203, 'train/grad_norm': 0.7573395371437073, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 12:26:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:26:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:08] train_sft.py:596 - step 514: {'train/loss': 0.14105987548828125, 'train/grad_norm': 0.9052619934082031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:26:08] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:26:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:12] train_sft.py:596 - step 515: {'train/loss': 0.14723587036132812, 'train/grad_norm': 0.8720011115074158, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:26:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:26:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:17] train_sft.py:596 - step 516: {'train/loss': 0.15394973754882812, 'train/grad_norm': 0.8495044708251953, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 12:26:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:26:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:23] train_sft.py:596 - step 517: {'train/loss': 0.13360595703125, 'train/grad_norm': 1.1909310817718506, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 12:26:23] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:26:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:28] train_sft.py:596 - step 518: {'train/loss': 0.12950515747070312, 'train/grad_norm': 1.0331636667251587, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:26:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:26:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:32] train_sft.py:596 - step 519: {'train/loss': 0.151092529296875, 'train/grad_norm': 1.0813424587249756, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:26:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:26:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:38] train_sft.py:596 - step 520: {'train/loss': 0.1313934326171875, 'train/grad_norm': 0.8846295475959778, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 12:26:38] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:26:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:42] train_sft.py:596 - step 521: {'train/loss': 0.1537628173828125, 'train/grad_norm': 0.7188400626182556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 12:26:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:26:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:48] train_sft.py:596 - step 522: {'train/loss': 0.13396167755126953, 'train/grad_norm': 0.8762580156326294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 12:26:48] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:26:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:52] train_sft.py:596 - step 523: {'train/loss': 0.13523101806640625, 'train/grad_norm': 0.7543925046920776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:26:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:26:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:26:57] train_sft.py:596 - step 524: {'train/loss': 0.15879440307617188, 'train/grad_norm': 1.2367550134658813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:26:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:26:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:02] train_sft.py:596 - step 525: {'train/loss': 0.1465473175048828, 'train/grad_norm': 0.6465587615966797, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 12:27:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:27:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:07] train_sft.py:596 - step 526: {'train/loss': 0.13721847534179688, 'train/grad_norm': 0.7610626220703125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:27:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:27:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:12] train_sft.py:596 - step 527: {'train/loss': 0.1370410919189453, 'train/grad_norm': 0.6931253671646118, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 12:27:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:27:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:16] train_sft.py:596 - step 528: {'train/loss': 0.15250015258789062, 'train/grad_norm': 0.7851636409759521, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 12:27:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:27:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:21] train_sft.py:596 - step 529: {'train/loss': 0.147857666015625, 'train/grad_norm': 0.9422017931938171, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:27:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:27:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:26] train_sft.py:596 - step 530: {'train/loss': 0.145538330078125, 'train/grad_norm': 0.7873724699020386, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:27:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:27:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:31] train_sft.py:596 - step 531: {'train/loss': 0.1577606201171875, 'train/grad_norm': 0.6830907464027405, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:27:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:27:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:36] train_sft.py:596 - step 532: {'train/loss': 0.13270950317382812, 'train/grad_norm': 1.1832536458969116, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 12:27:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:27:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:41] train_sft.py:596 - step 533: {'train/loss': 0.17004966735839844, 'train/grad_norm': 0.8354672193527222, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:27:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:27:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:45] train_sft.py:596 - step 534: {'train/loss': 0.12796783447265625, 'train/grad_norm': 0.7254929542541504, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 12:27:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:27:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:50] train_sft.py:596 - step 535: {'train/loss': 0.13892364501953125, 'train/grad_norm': 0.7272843718528748, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 12:27:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:27:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:27:55] train_sft.py:596 - step 536: {'train/loss': 0.13827133178710938, 'train/grad_norm': 0.764721691608429, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:27:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:27:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:00] train_sft.py:596 - step 537: {'train/loss': 0.1465306282043457, 'train/grad_norm': 2.460341691970825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 12:28:00] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:28:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:04] train_sft.py:596 - step 538: {'train/loss': 0.11401247978210449, 'train/grad_norm': 1.4172478914260864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 12:28:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:28:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:09] train_sft.py:596 - step 539: {'train/loss': 0.157501220703125, 'train/grad_norm': 0.8949230313301086, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:28:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:28:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:14] train_sft.py:596 - step 540: {'train/loss': 0.13866901397705078, 'train/grad_norm': 0.9002851247787476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 12:28:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:28:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:19] train_sft.py:596 - step 541: {'train/loss': 0.14101409912109375, 'train/grad_norm': 1.0437055826187134, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:28:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:28:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:24] train_sft.py:596 - step 542: {'train/loss': 0.1821746826171875, 'train/grad_norm': 0.7146018743515015, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:28:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:28:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:28] train_sft.py:596 - step 543: {'train/loss': 0.14019775390625, 'train/grad_norm': 0.5632349848747253, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:28:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:28:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:33] train_sft.py:596 - step 544: {'train/loss': 0.11641120910644531, 'train/grad_norm': 0.6947276592254639, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 12:28:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:28:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:38] train_sft.py:596 - step 545: {'train/loss': 0.13141632080078125, 'train/grad_norm': 0.9315513968467712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:28:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:28:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:42] train_sft.py:596 - step 546: {'train/loss': 0.16193389892578125, 'train/grad_norm': 1.2023519277572632, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:28:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:28:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:47] train_sft.py:596 - step 547: {'train/loss': 0.137969970703125, 'train/grad_norm': 1.0346872806549072, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:28:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:28:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:51] train_sft.py:596 - step 548: {'train/loss': 0.14873504638671875, 'train/grad_norm': 0.7108237147331238, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:28:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:28:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:28:56] train_sft.py:596 - step 549: {'train/loss': 0.1266326904296875, 'train/grad_norm': 0.7815576195716858, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:28:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:28:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:01] train_sft.py:596 - step 550: {'train/loss': 0.14505958557128906, 'train/grad_norm': 0.9082220196723938, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:29:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:05] train_sft.py:596 - step 551: {'train/loss': 0.14258575439453125, 'train/grad_norm': 0.8615723848342896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:29:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:10] train_sft.py:596 - step 552: {'train/loss': 0.12688064575195312, 'train/grad_norm': 1.0945221185684204, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:29:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:15] train_sft.py:596 - step 553: {'train/loss': 0.13854360580444336, 'train/grad_norm': 0.8042090535163879, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 12:29:15] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:29:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:20] train_sft.py:596 - step 554: {'train/loss': 0.15903091430664062, 'train/grad_norm': 1.4948070049285889, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:29:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:24] train_sft.py:596 - step 555: {'train/loss': 0.15288352966308594, 'train/grad_norm': 1.6237539052963257, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:29:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:29] train_sft.py:596 - step 556: {'train/loss': 0.14059948921203613, 'train/grad_norm': 0.7712233662605286, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:29:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:34] train_sft.py:596 - step 557: {'train/loss': 0.11797332763671875, 'train/grad_norm': 0.9610700011253357, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:29:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:38] train_sft.py:596 - step 558: {'train/loss': 0.12807226181030273, 'train/grad_norm': 0.9181515574455261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:29:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:43] train_sft.py:596 - step 559: {'train/loss': 0.15454864501953125, 'train/grad_norm': 1.4131137132644653, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:29:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:48] train_sft.py:596 - step 560: {'train/loss': 0.1563720703125, 'train/grad_norm': 0.9438891410827637, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:29:48] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:29:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:52] train_sft.py:596 - step 561: {'train/loss': 0.14216995239257812, 'train/grad_norm': 1.235889196395874, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:29:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:29:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:29:57] train_sft.py:596 - step 562: {'train/loss': 0.12612152099609375, 'train/grad_norm': 0.8232814073562622, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:29:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:29:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:02] train_sft.py:596 - step 563: {'train/loss': 0.15930938720703125, 'train/grad_norm': 1.2205804586410522, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:30:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:30:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:06] train_sft.py:596 - step 564: {'train/loss': 0.15662384033203125, 'train/grad_norm': 0.9283667802810669, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:30:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:11] train_sft.py:596 - step 565: {'train/loss': 0.136749267578125, 'train/grad_norm': 0.9618251323699951, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:30:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:16] train_sft.py:596 - step 566: {'train/loss': 0.1311655044555664, 'train/grad_norm': 0.7105769515037537, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:30:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:20] train_sft.py:596 - step 567: {'train/loss': 0.1405487060546875, 'train/grad_norm': 1.1859229803085327, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:30:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:25] train_sft.py:596 - step 568: {'train/loss': 0.14635276794433594, 'train/grad_norm': 0.7550866007804871, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:30:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:30] train_sft.py:596 - step 569: {'train/loss': 0.1318531036376953, 'train/grad_norm': 0.8691140413284302, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 12:30:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:30:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:34] train_sft.py:596 - step 570: {'train/loss': 0.16930389404296875, 'train/grad_norm': 1.1311893463134766, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:30:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:39] train_sft.py:596 - step 571: {'train/loss': 0.12625980377197266, 'train/grad_norm': 0.8337391018867493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:30:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:44] train_sft.py:596 - step 572: {'train/loss': 0.137847900390625, 'train/grad_norm': 1.057259202003479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:30:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:48] train_sft.py:596 - step 573: {'train/loss': 0.13852500915527344, 'train/grad_norm': 0.7440211772918701, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:30:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:30:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:53] train_sft.py:596 - step 574: {'train/loss': 0.1386566162109375, 'train/grad_norm': 0.8624892830848694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:30:53] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:30:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:30:57] train_sft.py:596 - step 575: {'train/loss': 0.15030670166015625, 'train/grad_norm': 1.2348474264144897, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:30:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:30:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:02] train_sft.py:596 - step 576: {'train/loss': 0.15263080596923828, 'train/grad_norm': 1.182234525680542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:31:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:31:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:07] train_sft.py:596 - step 577: {'train/loss': 0.1756134033203125, 'train/grad_norm': 0.6856321096420288, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:31:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:31:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:11] train_sft.py:596 - step 578: {'train/loss': 0.12791824340820312, 'train/grad_norm': 1.1450315713882446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:31:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:16] train_sft.py:596 - step 579: {'train/loss': 0.14963531494140625, 'train/grad_norm': 0.7334660291671753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:31:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:21] train_sft.py:596 - step 580: {'train/loss': 0.15851974487304688, 'train/grad_norm': 1.1181071996688843, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:31:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:31:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:25] train_sft.py:596 - step 581: {'train/loss': 0.16142654418945312, 'train/grad_norm': 1.1801657676696777, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:31:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:31:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:30] train_sft.py:596 - step 582: {'train/loss': 0.13383865356445312, 'train/grad_norm': 1.0503700971603394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:31:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:35] train_sft.py:596 - step 583: {'train/loss': 0.1480865478515625, 'train/grad_norm': 1.2528975009918213, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:31:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:39] train_sft.py:596 - step 584: {'train/loss': 0.12498092651367188, 'train/grad_norm': 0.856005847454071, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:31:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:44] train_sft.py:596 - step 585: {'train/loss': 0.12563323974609375, 'train/grad_norm': 0.8630613684654236, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:31:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:49] train_sft.py:596 - step 586: {'train/loss': 0.1414775848388672, 'train/grad_norm': 1.4077119827270508, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:31:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:53] train_sft.py:596 - step 587: {'train/loss': 0.1232757568359375, 'train/grad_norm': 0.8523463606834412, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:31:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:31:58] train_sft.py:596 - step 588: {'train/loss': 0.11742973327636719, 'train/grad_norm': 0.567970871925354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:31:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:31:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:03] train_sft.py:596 - step 589: {'train/loss': 0.16078948974609375, 'train/grad_norm': 0.7987444996833801, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:32:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:32:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:07] train_sft.py:596 - step 590: {'train/loss': 0.1288909912109375, 'train/grad_norm': 0.8136930465698242, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:32:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:32:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:12] train_sft.py:596 - step 591: {'train/loss': 0.15975189208984375, 'train/grad_norm': 0.8575620055198669, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:32:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:17] train_sft.py:596 - step 592: {'train/loss': 0.13330841064453125, 'train/grad_norm': 0.8415536284446716, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 12:32:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:32:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:21] train_sft.py:596 - step 593: {'train/loss': 0.1373748779296875, 'train/grad_norm': 1.035640001296997, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:32:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:26] train_sft.py:596 - step 594: {'train/loss': 0.1602792739868164, 'train/grad_norm': 1.1219359636306763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:32:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:31] train_sft.py:596 - step 595: {'train/loss': 0.16916656494140625, 'train/grad_norm': 1.3975751399993896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:32:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:32:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:35] train_sft.py:596 - step 596: {'train/loss': 0.15977096557617188, 'train/grad_norm': 0.84806227684021, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:32:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:40] train_sft.py:596 - step 597: {'train/loss': 0.1483154296875, 'train/grad_norm': 2.1575300693511963, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:32:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:32:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:44] train_sft.py:596 - step 598: {'train/loss': 0.16036415100097656, 'train/grad_norm': 0.8441912531852722, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:32:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:49] train_sft.py:596 - step 599: {'train/loss': 0.15239715576171875, 'train/grad_norm': 1.09963858127594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:32:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:54] train_sft.py:596 - step 600: {'train/loss': 0.131866455078125, 'train/grad_norm': 0.6287695169448853, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:32:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:32:58] train_sft.py:596 - step 601: {'train/loss': 0.1310100555419922, 'train/grad_norm': 0.715436577796936, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:32:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:32:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:03] train_sft.py:596 - step 602: {'train/loss': 0.1410675048828125, 'train/grad_norm': 1.1108952760696411, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:33:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:08] train_sft.py:596 - step 603: {'train/loss': 0.1366119384765625, 'train/grad_norm': 1.1254782676696777, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 12:33:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:33:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:13] train_sft.py:596 - step 604: {'train/loss': 0.12412643432617188, 'train/grad_norm': 0.7751631140708923, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:33:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:17] train_sft.py:596 - step 605: {'train/loss': 0.1402285099029541, 'train/grad_norm': 0.849436342716217, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:33:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:22] train_sft.py:596 - step 606: {'train/loss': 0.15330123901367188, 'train/grad_norm': 0.8038697838783264, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:33:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:33:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:27] train_sft.py:596 - step 607: {'train/loss': 0.1605682373046875, 'train/grad_norm': 1.2982892990112305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:33:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:31] train_sft.py:596 - step 608: {'train/loss': 0.17198562622070312, 'train/grad_norm': 0.9410358667373657, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:33:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:36] train_sft.py:596 - step 609: {'train/loss': 0.1435089111328125, 'train/grad_norm': 0.9568890929222107, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:33:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:33:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:41] train_sft.py:596 - step 610: {'train/loss': 0.15735626220703125, 'train/grad_norm': 0.5885907411575317, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:33:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:45] train_sft.py:596 - step 611: {'train/loss': 0.13584470748901367, 'train/grad_norm': 0.8980787396430969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:33:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:50] train_sft.py:596 - step 612: {'train/loss': 0.16413116455078125, 'train/grad_norm': 1.507311224937439, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:33:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:33:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:54] train_sft.py:596 - step 613: {'train/loss': 0.14392471313476562, 'train/grad_norm': 0.6193671226501465, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:33:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:33:59] train_sft.py:596 - step 614: {'train/loss': 0.15877914428710938, 'train/grad_norm': 0.6036980152130127, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:33:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:33:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:04] train_sft.py:596 - step 615: {'train/loss': 0.1483001708984375, 'train/grad_norm': 0.709610641002655, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:34:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:08] train_sft.py:596 - step 616: {'train/loss': 0.15030670166015625, 'train/grad_norm': 1.160668134689331, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:34:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:13] train_sft.py:596 - step 617: {'train/loss': 0.17020034790039062, 'train/grad_norm': 1.0558744668960571, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:34:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:18] train_sft.py:596 - step 618: {'train/loss': 0.16544342041015625, 'train/grad_norm': 0.6373021602630615, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:34:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:22] train_sft.py:596 - step 619: {'train/loss': 0.14458465576171875, 'train/grad_norm': 1.1260905265808105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:34:22] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:34:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:27] train_sft.py:596 - step 620: {'train/loss': 0.15173912048339844, 'train/grad_norm': 0.859058678150177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:34:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:32] train_sft.py:596 - step 621: {'train/loss': 0.13470458984375, 'train/grad_norm': 0.5536190271377563, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:34:32] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:34:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:36] train_sft.py:596 - step 622: {'train/loss': 0.14581298828125, 'train/grad_norm': 0.8195598721504211, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:34:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:41] train_sft.py:596 - step 623: {'train/loss': 0.18174362182617188, 'train/grad_norm': 0.7339203953742981, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:34:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:46] train_sft.py:596 - step 624: {'train/loss': 0.19684600830078125, 'train/grad_norm': 1.8408278226852417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:34:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:51] train_sft.py:596 - step 625: {'train/loss': 0.16412138938903809, 'train/grad_norm': 0.8632243275642395, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:34:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:34:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:34:55] train_sft.py:596 - step 626: {'train/loss': 0.1446075439453125, 'train/grad_norm': 0.6063505411148071, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:34:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:34:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:00] train_sft.py:596 - step 627: {'train/loss': 0.1387939453125, 'train/grad_norm': 0.7598285675048828, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:35:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:35:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:04] train_sft.py:596 - step 628: {'train/loss': 0.1428070068359375, 'train/grad_norm': 0.9964464902877808, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:35:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:35:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:09] train_sft.py:596 - step 629: {'train/loss': 0.13390350341796875, 'train/grad_norm': 1.202257752418518, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:35:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:35:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:14] train_sft.py:596 - step 630: {'train/loss': 0.14208221435546875, 'train/grad_norm': 0.7707167863845825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:35:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:35:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:19] train_sft.py:596 - step 631: {'train/loss': 0.15320587158203125, 'train/grad_norm': 0.9991748332977295, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:35:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:35:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:23] train_sft.py:596 - step 632: {'train/loss': 0.12556838989257812, 'train/grad_norm': 0.5818033814430237, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:35:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:35:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:29] train_sft.py:596 - step 633: {'train/loss': 0.13942241668701172, 'train/grad_norm': 0.9341635704040527, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 12:35:29] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:35:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:33] train_sft.py:596 - step 634: {'train/loss': 0.14165496826171875, 'train/grad_norm': 1.008209228515625, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 12:35:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:35:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:38] train_sft.py:596 - step 635: {'train/loss': 0.12967300415039062, 'train/grad_norm': 0.607965350151062, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:35:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:35:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:43] train_sft.py:596 - step 636: {'train/loss': 0.1465301513671875, 'train/grad_norm': 0.8955593109130859, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:35:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:35:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:47] train_sft.py:596 - step 637: {'train/loss': 0.15468215942382812, 'train/grad_norm': 0.707093358039856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:35:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:35:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:52] train_sft.py:596 - step 638: {'train/loss': 0.15071487426757812, 'train/grad_norm': 0.9418709874153137, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:35:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:35:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:35:57] train_sft.py:596 - step 639: {'train/loss': 0.140960693359375, 'train/grad_norm': 0.8418763279914856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 12:35:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:35:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:02] train_sft.py:596 - step 640: {'train/loss': 0.137542724609375, 'train/grad_norm': 1.4031305313110352, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:36:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:36:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:07] train_sft.py:596 - step 641: {'train/loss': 0.15728759765625, 'train/grad_norm': 0.8406047821044922, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 12:36:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:36:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:12] train_sft.py:596 - step 642: {'train/loss': 0.12938690185546875, 'train/grad_norm': 0.5858879685401917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 12:36:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:36:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:16] train_sft.py:596 - step 643: {'train/loss': 0.13373184204101562, 'train/grad_norm': 0.5765774250030518, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:36:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:36:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:21] train_sft.py:596 - step 644: {'train/loss': 0.1479034423828125, 'train/grad_norm': 0.6612336039543152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 12:36:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:36:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:26] train_sft.py:596 - step 645: {'train/loss': 0.12222814559936523, 'train/grad_norm': 0.6505294442176819, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:36:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:36:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:31] train_sft.py:596 - step 646: {'train/loss': 0.1408538818359375, 'train/grad_norm': 0.8624991774559021, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:36:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:36:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:36] train_sft.py:596 - step 647: {'train/loss': 0.12623023986816406, 'train/grad_norm': 0.678499162197113, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 12:36:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:36:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:40] train_sft.py:596 - step 648: {'train/loss': 0.18532562255859375, 'train/grad_norm': 1.7225216627120972, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:36:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:36:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:45] train_sft.py:596 - step 649: {'train/loss': 0.12267684936523438, 'train/grad_norm': 0.8612110018730164, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 12:36:45] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:36:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:50] train_sft.py:596 - step 650: {'train/loss': 0.1287994384765625, 'train/grad_norm': 0.7495962381362915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:36:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:36:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:36:55] train_sft.py:596 - step 651: {'train/loss': 0.12504148483276367, 'train/grad_norm': 0.825620710849762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:36:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:36:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:00] train_sft.py:596 - step 652: {'train/loss': 0.1411590576171875, 'train/grad_norm': 0.8113839626312256, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:37:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:37:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:05] train_sft.py:596 - step 653: {'train/loss': 0.12702178955078125, 'train/grad_norm': 0.6487047076225281, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:37:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:37:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:10] train_sft.py:596 - step 654: {'train/loss': 0.138946533203125, 'train/grad_norm': 0.8815346360206604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 12:37:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:37:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:15] train_sft.py:596 - step 655: {'train/loss': 0.13825607299804688, 'train/grad_norm': 1.0990244150161743, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 12:37:15] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:37:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:20] train_sft.py:596 - step 656: {'train/loss': 0.14753246307373047, 'train/grad_norm': 1.1256515979766846, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 12:37:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:37:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:24] train_sft.py:596 - step 657: {'train/loss': 0.14286231994628906, 'train/grad_norm': 0.8641185760498047, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 12:37:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:37:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:29] train_sft.py:596 - step 658: {'train/loss': 0.16884231567382812, 'train/grad_norm': 0.8241091966629028, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:37:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:37:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:34] train_sft.py:596 - step 659: {'train/loss': 0.13933563232421875, 'train/grad_norm': 0.8049700856208801, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 12:37:34] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:37:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:39] train_sft.py:596 - step 660: {'train/loss': 0.14055252075195312, 'train/grad_norm': 0.7419324517250061, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:37:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:37:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:44] train_sft.py:596 - step 661: {'train/loss': 0.13399696350097656, 'train/grad_norm': 0.7814229130744934, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:37:44] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:37:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:49] train_sft.py:596 - step 662: {'train/loss': 0.1353282928466797, 'train/grad_norm': 1.2455554008483887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:37:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:37:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:54] train_sft.py:596 - step 663: {'train/loss': 0.1581268310546875, 'train/grad_norm': 0.910771369934082, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:37:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:37:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:37:58] train_sft.py:596 - step 664: {'train/loss': 0.13764190673828125, 'train/grad_norm': 0.9208259582519531, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:37:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:37:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:03] train_sft.py:596 - step 665: {'train/loss': 0.13251876831054688, 'train/grad_norm': 0.6690730452537537, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:38:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:38:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:08] train_sft.py:596 - step 666: {'train/loss': 0.14187908172607422, 'train/grad_norm': 1.1386187076568604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:38:08] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:38:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:12] train_sft.py:596 - step 667: {'train/loss': 0.15428733825683594, 'train/grad_norm': 1.173606276512146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:38:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:38:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:17] train_sft.py:596 - step 668: {'train/loss': 0.15132904052734375, 'train/grad_norm': 0.8828467130661011, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:38:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:38:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:22] train_sft.py:596 - step 669: {'train/loss': 0.13690757751464844, 'train/grad_norm': 1.095133900642395, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 12:38:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:38:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:27] train_sft.py:596 - step 670: {'train/loss': 0.1461944580078125, 'train/grad_norm': 1.0452834367752075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:38:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:38:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:32] train_sft.py:596 - step 671: {'train/loss': 0.14993858337402344, 'train/grad_norm': 1.3594071865081787, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 12:38:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:38:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:37] train_sft.py:596 - step 672: {'train/loss': 0.12363815307617188, 'train/grad_norm': 1.0851373672485352, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 12:38:37] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:38:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:42] train_sft.py:596 - step 673: {'train/loss': 0.14332962036132812, 'train/grad_norm': 0.810524582862854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:38:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:38:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:47] train_sft.py:596 - step 674: {'train/loss': 0.14707374572753906, 'train/grad_norm': 0.8394205570220947, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:38:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:38:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:52] train_sft.py:596 - step 675: {'train/loss': 0.15688800811767578, 'train/grad_norm': 1.0231696367263794, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:38:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:38:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:38:57] train_sft.py:596 - step 676: {'train/loss': 0.08286619186401367, 'train/grad_norm': 0.6028135418891907, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:38:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:38:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:02] train_sft.py:596 - step 677: {'train/loss': 0.11051559448242188, 'train/grad_norm': 1.1346666812896729, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:39:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:39:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:07] train_sft.py:596 - step 678: {'train/loss': 0.10031509399414062, 'train/grad_norm': 1.677443504333496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 12:39:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:39:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:13] train_sft.py:596 - step 679: {'train/loss': 0.08969306945800781, 'train/grad_norm': 0.745026171207428, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.64s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.64s/it]
[2025-12-29 12:39:13] timer.py:32 - Timer actor_train end (elapsed: 5.6s)
[2025-12-29 12:39:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:18] train_sft.py:596 - step 680: {'train/loss': 0.06852340698242188, 'train/grad_norm': 0.45946288108825684, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:39:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:39:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:22] train_sft.py:596 - step 681: {'train/loss': 0.0741434097290039, 'train/grad_norm': 0.6037951111793518, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:39:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:39:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:28] train_sft.py:596 - step 682: {'train/loss': 0.11974525451660156, 'train/grad_norm': 1.3538391590118408, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 12:39:28] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:39:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:33] train_sft.py:596 - step 683: {'train/loss': 0.10070037841796875, 'train/grad_norm': 1.0011013746261597, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:39:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:39:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:37] train_sft.py:596 - step 684: {'train/loss': 0.08351325988769531, 'train/grad_norm': 0.7835560441017151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 12:39:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:39:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:42] train_sft.py:596 - step 685: {'train/loss': 0.0924835205078125, 'train/grad_norm': 1.2085987329483032, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 12:39:42] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:39:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:48] train_sft.py:596 - step 686: {'train/loss': 0.07660090923309326, 'train/grad_norm': 0.7562618851661682, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.12s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.13s/it]
[2025-12-29 12:39:48] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:39:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:53] train_sft.py:596 - step 687: {'train/loss': 0.09761911630630493, 'train/grad_norm': 1.1572611331939697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 12:39:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:39:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:39:58] train_sft.py:596 - step 688: {'train/loss': 0.10069406032562256, 'train/grad_norm': 0.8081592917442322, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 12:39:58] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:39:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:03] train_sft.py:596 - step 689: {'train/loss': 0.08945941925048828, 'train/grad_norm': 0.7654396295547485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:40:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:40:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:07] train_sft.py:596 - step 690: {'train/loss': 0.08656835556030273, 'train/grad_norm': 0.9656816124916077, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:40:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:40:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:12] train_sft.py:596 - step 691: {'train/loss': 0.11614608764648438, 'train/grad_norm': 1.3567566871643066, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:40:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:40:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:17] train_sft.py:596 - step 692: {'train/loss': 0.08628034591674805, 'train/grad_norm': 0.7037297487258911, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:40:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:40:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:23] train_sft.py:596 - step 693: {'train/loss': 0.09057474136352539, 'train/grad_norm': 0.9725209474563599, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 12:40:23] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:40:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:28] train_sft.py:596 - step 694: {'train/loss': 0.08788776397705078, 'train/grad_norm': 0.762362539768219, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.29s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.29s/it]
[2025-12-29 12:40:28] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 12:40:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:33] train_sft.py:596 - step 695: {'train/loss': 0.08819770812988281, 'train/grad_norm': 0.9649757146835327, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:40:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:40:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:38] train_sft.py:596 - step 696: {'train/loss': 0.1042327880859375, 'train/grad_norm': 1.0827219486236572, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 12:40:38] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:40:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:43] train_sft.py:596 - step 697: {'train/loss': 0.08244132995605469, 'train/grad_norm': 0.892158031463623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:40:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:40:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:47] train_sft.py:596 - step 698: {'train/loss': 0.09979033470153809, 'train/grad_norm': 0.6703581213951111, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:40:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:40:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:53] train_sft.py:596 - step 699: {'train/loss': 0.10478401184082031, 'train/grad_norm': 0.8716660737991333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 12:40:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:40:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:40:57] train_sft.py:596 - step 700: {'train/loss': 0.09312820434570312, 'train/grad_norm': 0.6903177499771118, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:40:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:40:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:02] train_sft.py:596 - step 701: {'train/loss': 0.08359146118164062, 'train/grad_norm': 0.9203430414199829, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 12:41:02] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:41:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:07] train_sft.py:596 - step 702: {'train/loss': 0.0800180435180664, 'train/grad_norm': 1.079975962638855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:41:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:41:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:12] train_sft.py:596 - step 703: {'train/loss': 0.0880478024482727, 'train/grad_norm': 1.0658047199249268, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 12:41:12] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:41:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:17] train_sft.py:596 - step 704: {'train/loss': 0.088442862033844, 'train/grad_norm': 0.6733609437942505, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 12:41:17] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:41:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:22] train_sft.py:596 - step 705: {'train/loss': 0.10829782485961914, 'train/grad_norm': 0.8158192038536072, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 12:41:22] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:41:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:27] train_sft.py:596 - step 706: {'train/loss': 0.06789016723632812, 'train/grad_norm': 0.6110751628875732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 12:41:27] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:41:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:32] train_sft.py:596 - step 707: {'train/loss': 0.09631156921386719, 'train/grad_norm': 1.0479758977890015, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 12:41:32] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:41:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:37] train_sft.py:596 - step 708: {'train/loss': 0.0768289566040039, 'train/grad_norm': 0.707057535648346, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:41:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:41:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:42] train_sft.py:596 - step 709: {'train/loss': 0.07547593116760254, 'train/grad_norm': 0.8059064149856567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:41:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:41:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:47] train_sft.py:596 - step 710: {'train/loss': 0.092254638671875, 'train/grad_norm': 0.7568475008010864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:41:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:41:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:52] train_sft.py:596 - step 711: {'train/loss': 0.08386802673339844, 'train/grad_norm': 1.0173897743225098, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:41:52] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:41:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:41:57] train_sft.py:596 - step 712: {'train/loss': 0.08550643920898438, 'train/grad_norm': 0.840640127658844, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 12:41:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:41:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:02] train_sft.py:596 - step 713: {'train/loss': 0.0772395133972168, 'train/grad_norm': 0.7907421588897705, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:42:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:07] train_sft.py:596 - step 714: {'train/loss': 0.0908203125, 'train/grad_norm': 0.8348450660705566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 12:42:07] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:42:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:11] train_sft.py:596 - step 715: {'train/loss': 0.09691110253334045, 'train/grad_norm': 0.7238988280296326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:42:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:16] train_sft.py:596 - step 716: {'train/loss': 0.12047958374023438, 'train/grad_norm': 2.0664448738098145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:42:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:42:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:21] train_sft.py:596 - step 717: {'train/loss': 0.10265302658081055, 'train/grad_norm': 0.7560257315635681, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:42:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:42:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:26] train_sft.py:596 - step 718: {'train/loss': 0.07817566394805908, 'train/grad_norm': 0.9015364646911621, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 12:42:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:42:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:30] train_sft.py:596 - step 719: {'train/loss': 0.10453033447265625, 'train/grad_norm': 0.8290688395500183, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:42:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:42:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:35] train_sft.py:596 - step 720: {'train/loss': 0.09517908096313477, 'train/grad_norm': 0.8613569736480713, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:42:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:40] train_sft.py:596 - step 721: {'train/loss': 0.09269523620605469, 'train/grad_norm': 0.8244480490684509, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:42:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:44] train_sft.py:596 - step 722: {'train/loss': 0.09194183349609375, 'train/grad_norm': 0.7042821645736694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:42:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:49] train_sft.py:596 - step 723: {'train/loss': 0.09946298599243164, 'train/grad_norm': 0.6820694208145142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:42:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:54] train_sft.py:596 - step 724: {'train/loss': 0.09271165728569031, 'train/grad_norm': 0.6949277520179749, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:42:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:42:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:42:58] train_sft.py:596 - step 725: {'train/loss': 0.09221625328063965, 'train/grad_norm': 0.6033307909965515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:42:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:42:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:03] train_sft.py:596 - step 726: {'train/loss': 0.08891648054122925, 'train/grad_norm': 0.638698935508728, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:43:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:43:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:08] train_sft.py:596 - step 727: {'train/loss': 0.09260940551757812, 'train/grad_norm': 0.7368608713150024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:43:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:12] train_sft.py:596 - step 728: {'train/loss': 0.08818435668945312, 'train/grad_norm': 0.6135103106498718, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:43:12] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:43:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:17] train_sft.py:596 - step 729: {'train/loss': 0.09634780883789062, 'train/grad_norm': 0.937978982925415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:43:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:22] train_sft.py:596 - step 730: {'train/loss': 0.0967569351196289, 'train/grad_norm': 0.6864917874336243, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 12:43:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:43:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:27] train_sft.py:596 - step 731: {'train/loss': 0.09608781337738037, 'train/grad_norm': 0.9683032631874084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:43:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:31] train_sft.py:596 - step 732: {'train/loss': 0.08577609062194824, 'train/grad_norm': 0.8266818523406982, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:43:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:36] train_sft.py:596 - step 733: {'train/loss': 0.07984888553619385, 'train/grad_norm': 0.9292714595794678, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:43:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:43:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:40] train_sft.py:596 - step 734: {'train/loss': 0.09671163558959961, 'train/grad_norm': 0.7990292906761169, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:43:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:43:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:45] train_sft.py:596 - step 735: {'train/loss': 0.0828399658203125, 'train/grad_norm': 1.1162159442901611, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:43:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:50] train_sft.py:596 - step 736: {'train/loss': 0.09289240837097168, 'train/grad_norm': 1.3983381986618042, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:43:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:54] train_sft.py:596 - step 737: {'train/loss': 0.097259521484375, 'train/grad_norm': 1.1449785232543945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:43:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:43:59] train_sft.py:596 - step 738: {'train/loss': 0.07756161689758301, 'train/grad_norm': 0.8568960428237915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:43:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:43:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:04] train_sft.py:596 - step 739: {'train/loss': 0.08037114143371582, 'train/grad_norm': 0.6547762751579285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:44:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:44:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:08] train_sft.py:596 - step 740: {'train/loss': 0.09377026557922363, 'train/grad_norm': 1.2112054824829102, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:44:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:13] train_sft.py:596 - step 741: {'train/loss': 0.09240132570266724, 'train/grad_norm': 0.9367436170578003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:44:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:18] train_sft.py:596 - step 742: {'train/loss': 0.10498046875, 'train/grad_norm': 0.96807461977005, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:44:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:22] train_sft.py:596 - step 743: {'train/loss': 0.09743499755859375, 'train/grad_norm': 0.8758026361465454, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:44:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:27] train_sft.py:596 - step 744: {'train/loss': 0.10193252563476562, 'train/grad_norm': 0.9159573316574097, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:44:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:32] train_sft.py:596 - step 745: {'train/loss': 0.09194374084472656, 'train/grad_norm': 0.9407172203063965, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:44:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:44:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:36] train_sft.py:596 - step 746: {'train/loss': 0.08180761337280273, 'train/grad_norm': 0.7399447560310364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:44:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:44:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:41] train_sft.py:596 - step 747: {'train/loss': 0.08867263793945312, 'train/grad_norm': 0.9167160391807556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:44:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:45] train_sft.py:596 - step 748: {'train/loss': 0.08840751647949219, 'train/grad_norm': 0.6345388889312744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:44:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:44:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:50] train_sft.py:596 - step 749: {'train/loss': 0.09193611145019531, 'train/grad_norm': 0.6716232895851135, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:44:50] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:44:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:44:55] train_sft.py:596 - step 750: {'train/loss': 0.09515380859375, 'train/grad_norm': 0.9896685481071472, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:44:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:44:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:00] train_sft.py:596 - step 751: {'train/loss': 0.09127044677734375, 'train/grad_norm': 0.8789017200469971, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:45:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:45:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:04] train_sft.py:596 - step 752: {'train/loss': 0.0983433723449707, 'train/grad_norm': 0.737794816493988, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:45:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:09] train_sft.py:596 - step 753: {'train/loss': 0.11896133422851562, 'train/grad_norm': 1.0445805788040161, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:45:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:14] train_sft.py:596 - step 754: {'train/loss': 0.08597946166992188, 'train/grad_norm': 0.9632170796394348, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:45:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:18] train_sft.py:596 - step 755: {'train/loss': 0.09152662754058838, 'train/grad_norm': 0.6907788515090942, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:45:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:45:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:23] train_sft.py:596 - step 756: {'train/loss': 0.08459615707397461, 'train/grad_norm': 0.9158426523208618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:45:23] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:45:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:28] train_sft.py:596 - step 757: {'train/loss': 0.09932518005371094, 'train/grad_norm': 0.5876821279525757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:45:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:32] train_sft.py:596 - step 758: {'train/loss': 0.0896148681640625, 'train/grad_norm': 0.9211333990097046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:45:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:37] train_sft.py:596 - step 759: {'train/loss': 0.09778022766113281, 'train/grad_norm': 0.7277283072471619, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:45:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:45:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:42] train_sft.py:596 - step 760: {'train/loss': 0.10062789916992188, 'train/grad_norm': 0.6868321299552917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:45:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:46] train_sft.py:596 - step 761: {'train/loss': 0.08889389038085938, 'train/grad_norm': 1.0339152812957764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:45:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:51] train_sft.py:596 - step 762: {'train/loss': 0.096771240234375, 'train/grad_norm': 0.6599830389022827, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 12:45:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:45:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:45:56] train_sft.py:596 - step 763: {'train/loss': 0.08231353759765625, 'train/grad_norm': 0.7323697209358215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:45:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:45:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:00] train_sft.py:596 - step 764: {'train/loss': 0.09447097778320312, 'train/grad_norm': 0.5627086162567139, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:46:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:46:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:05] train_sft.py:596 - step 765: {'train/loss': 0.1080312728881836, 'train/grad_norm': 1.1651668548583984, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:46:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:09] train_sft.py:596 - step 766: {'train/loss': 0.0841073989868164, 'train/grad_norm': 0.7533983588218689, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:46:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:46:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:14] train_sft.py:596 - step 767: {'train/loss': 0.08641433715820312, 'train/grad_norm': 0.7213518619537354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:46:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:19] train_sft.py:596 - step 768: {'train/loss': 0.0877685546875, 'train/grad_norm': 1.258693814277649, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:46:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:23] train_sft.py:596 - step 769: {'train/loss': 0.09021568298339844, 'train/grad_norm': 0.6167287826538086, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:46:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:28] train_sft.py:596 - step 770: {'train/loss': 0.07591629028320312, 'train/grad_norm': 0.6581539511680603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:46:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:33] train_sft.py:596 - step 771: {'train/loss': 0.0905303955078125, 'train/grad_norm': 0.6516328454017639, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:46:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:37] train_sft.py:596 - step 772: {'train/loss': 0.07919096946716309, 'train/grad_norm': 0.8066127896308899, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 12:46:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:46:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:42] train_sft.py:596 - step 773: {'train/loss': 0.08039283752441406, 'train/grad_norm': 1.225019931793213, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:46:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:47] train_sft.py:596 - step 774: {'train/loss': 0.08760452270507812, 'train/grad_norm': 0.8239264488220215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:46:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:51] train_sft.py:596 - step 775: {'train/loss': 0.08573627471923828, 'train/grad_norm': 0.9872370362281799, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:46:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:46:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:46:56] train_sft.py:596 - step 776: {'train/loss': 0.08869314193725586, 'train/grad_norm': 1.2282085418701172, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 12:46:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:46:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:01] train_sft.py:596 - step 777: {'train/loss': 0.10493594408035278, 'train/grad_norm': 1.3167462348937988, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 12:47:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:47:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:06] train_sft.py:596 - step 778: {'train/loss': 0.09461021423339844, 'train/grad_norm': 1.0283229351043701, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:47:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:10] train_sft.py:596 - step 779: {'train/loss': 0.10229611396789551, 'train/grad_norm': 0.7561266422271729, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:47:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:47:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:15] train_sft.py:596 - step 780: {'train/loss': 0.09018555283546448, 'train/grad_norm': 1.4316606521606445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:47:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:20] train_sft.py:596 - step 781: {'train/loss': 0.1179046630859375, 'train/grad_norm': 1.0000430345535278, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:47:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:24] train_sft.py:596 - step 782: {'train/loss': 0.11867141723632812, 'train/grad_norm': 0.9942494034767151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:47:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:29] train_sft.py:596 - step 783: {'train/loss': 0.10952377319335938, 'train/grad_norm': 0.7447072863578796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:47:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:47:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:34] train_sft.py:596 - step 784: {'train/loss': 0.1039729118347168, 'train/grad_norm': 1.2205253839492798, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:47:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:38] train_sft.py:596 - step 785: {'train/loss': 0.11006593704223633, 'train/grad_norm': 0.7130770683288574, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:47:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:43] train_sft.py:596 - step 786: {'train/loss': 0.08772027492523193, 'train/grad_norm': 0.7149430513381958, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:47:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:47] train_sft.py:596 - step 787: {'train/loss': 0.09191560745239258, 'train/grad_norm': 0.628165602684021, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:47:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:47:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:52] train_sft.py:596 - step 788: {'train/loss': 0.10429465770721436, 'train/grad_norm': 1.210903286933899, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:47:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:47:57] train_sft.py:596 - step 789: {'train/loss': 0.09940719604492188, 'train/grad_norm': 0.7326235771179199, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:47:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:47:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:01] train_sft.py:596 - step 790: {'train/loss': 0.10078811645507812, 'train/grad_norm': 0.6088418364524841, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:48:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:06] train_sft.py:596 - step 791: {'train/loss': 0.09119033813476562, 'train/grad_norm': 1.1258888244628906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:48:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:11] train_sft.py:596 - step 792: {'train/loss': 0.08917474746704102, 'train/grad_norm': 0.5848244428634644, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:48:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:15] train_sft.py:596 - step 793: {'train/loss': 0.09411478042602539, 'train/grad_norm': 0.7026198506355286, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 12:48:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:48:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:20] train_sft.py:596 - step 794: {'train/loss': 0.10810089111328125, 'train/grad_norm': 0.9936590194702148, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:48:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:25] train_sft.py:596 - step 795: {'train/loss': 0.09844541549682617, 'train/grad_norm': 0.7318400740623474, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:48:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:29] train_sft.py:596 - step 796: {'train/loss': 0.11178970336914062, 'train/grad_norm': 0.6713109612464905, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 12:48:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:48:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:34] train_sft.py:596 - step 797: {'train/loss': 0.10621261596679688, 'train/grad_norm': 0.5446645617485046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:48:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:39] train_sft.py:596 - step 798: {'train/loss': 0.07447624206542969, 'train/grad_norm': 0.6932899355888367, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:48:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:44] train_sft.py:596 - step 799: {'train/loss': 0.09016656875610352, 'train/grad_norm': 0.6910343170166016, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:48:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:48:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:48] train_sft.py:596 - step 800: {'train/loss': 0.1027059555053711, 'train/grad_norm': 1.0866775512695312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:48:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:53] train_sft.py:596 - step 801: {'train/loss': 0.08609199523925781, 'train/grad_norm': 0.8318400382995605, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:48:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:48:58] train_sft.py:596 - step 802: {'train/loss': 0.09602642059326172, 'train/grad_norm': 0.9021262526512146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:48:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:48:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:02] train_sft.py:596 - step 803: {'train/loss': 0.10278701782226562, 'train/grad_norm': 1.1625053882598877, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:49:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:07] train_sft.py:596 - step 804: {'train/loss': 0.09600448608398438, 'train/grad_norm': 1.106724500656128, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:49:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:12] train_sft.py:596 - step 805: {'train/loss': 0.08297538757324219, 'train/grad_norm': 0.569806694984436, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:49:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:16] train_sft.py:596 - step 806: {'train/loss': 0.10245513916015625, 'train/grad_norm': 0.7408463954925537, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 12:49:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:49:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:21] train_sft.py:596 - step 807: {'train/loss': 0.09163570404052734, 'train/grad_norm': 0.7625221610069275, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:49:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:26] train_sft.py:596 - step 808: {'train/loss': 0.1053471565246582, 'train/grad_norm': 0.8640251159667969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:49:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:30] train_sft.py:596 - step 809: {'train/loss': 0.10463905334472656, 'train/grad_norm': 0.8757048845291138, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:49:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:35] train_sft.py:596 - step 810: {'train/loss': 0.07590007781982422, 'train/grad_norm': 0.6109570860862732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:49:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:40] train_sft.py:596 - step 811: {'train/loss': 0.08771014213562012, 'train/grad_norm': 0.8580237030982971, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:49:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:49:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:44] train_sft.py:596 - step 812: {'train/loss': 0.09702491760253906, 'train/grad_norm': 0.8067721724510193, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:49:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:49] train_sft.py:596 - step 813: {'train/loss': 0.0981903076171875, 'train/grad_norm': 1.0708023309707642, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:49:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:54] train_sft.py:596 - step 814: {'train/loss': 0.09310531616210938, 'train/grad_norm': 0.9339520931243896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 12:49:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:49:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:49:58] train_sft.py:596 - step 815: {'train/loss': 0.08603394031524658, 'train/grad_norm': 0.6586425304412842, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:49:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:49:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:03] train_sft.py:596 - step 816: {'train/loss': 0.0945286750793457, 'train/grad_norm': 0.9012315273284912, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:50:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:08] train_sft.py:596 - step 817: {'train/loss': 0.10652732849121094, 'train/grad_norm': 0.9814567565917969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:50:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:13] train_sft.py:596 - step 818: {'train/loss': 0.08948230743408203, 'train/grad_norm': 0.7554709315299988, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 12:50:13] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:50:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:17] train_sft.py:596 - step 819: {'train/loss': 0.10846328735351562, 'train/grad_norm': 0.8684382438659668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:50:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:22] train_sft.py:596 - step 820: {'train/loss': 0.09568548202514648, 'train/grad_norm': 0.6233510971069336, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:50:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:27] train_sft.py:596 - step 821: {'train/loss': 0.09112310409545898, 'train/grad_norm': 0.8498476147651672, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 12:50:27] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:50:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:32] train_sft.py:596 - step 822: {'train/loss': 0.09054374694824219, 'train/grad_norm': 0.6965276598930359, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:50:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:37] train_sft.py:596 - step 823: {'train/loss': 0.10497045516967773, 'train/grad_norm': 0.7421517968177795, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:50:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:50:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:41] train_sft.py:596 - step 824: {'train/loss': 0.12098097801208496, 'train/grad_norm': 0.6351818442344666, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:50:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:46] train_sft.py:596 - step 825: {'train/loss': 0.07744574546813965, 'train/grad_norm': 0.5514991879463196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:50:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:50:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:51] train_sft.py:596 - step 826: {'train/loss': 0.08916854858398438, 'train/grad_norm': 0.6605522632598877, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 12:50:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:50:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:50:56] train_sft.py:596 - step 827: {'train/loss': 0.09411907196044922, 'train/grad_norm': 1.1098772287368774, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 12:50:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:50:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:00] train_sft.py:596 - step 828: {'train/loss': 0.09535884857177734, 'train/grad_norm': 1.2009629011154175, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 12:51:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:51:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:05] train_sft.py:596 - step 829: {'train/loss': 0.11012649536132812, 'train/grad_norm': 1.5642603635787964, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:51:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:51:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:10] train_sft.py:596 - step 830: {'train/loss': 0.0948333740234375, 'train/grad_norm': 0.9567161798477173, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:51:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:51:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:15] train_sft.py:596 - step 831: {'train/loss': 0.10178661346435547, 'train/grad_norm': 0.8398251533508301, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 12:51:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:51:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:20] train_sft.py:596 - step 832: {'train/loss': 0.08558464050292969, 'train/grad_norm': 0.8974823951721191, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:51:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:51:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:24] train_sft.py:596 - step 833: {'train/loss': 0.11102104187011719, 'train/grad_norm': 1.0434385538101196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 12:51:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:51:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:30] train_sft.py:596 - step 834: {'train/loss': 0.09662103652954102, 'train/grad_norm': 1.129998803138733, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.17s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.17s/it]
[2025-12-29 12:51:30] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 12:51:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:35] train_sft.py:596 - step 835: {'train/loss': 0.10150003433227539, 'train/grad_norm': 0.8152654767036438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:51:35] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:51:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:40] train_sft.py:596 - step 836: {'train/loss': 0.07945060729980469, 'train/grad_norm': 0.7575931549072266, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:51:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:51:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:44] train_sft.py:596 - step 837: {'train/loss': 0.10204315185546875, 'train/grad_norm': 0.6994389295578003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 12:51:44] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:51:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:49] train_sft.py:596 - step 838: {'train/loss': 0.10891413688659668, 'train/grad_norm': 0.8975235223770142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:51:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:51:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:54] train_sft.py:596 - step 839: {'train/loss': 0.10412216186523438, 'train/grad_norm': 1.0070719718933105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 12:51:54] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:51:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:51:59] train_sft.py:596 - step 840: {'train/loss': 0.09314346313476562, 'train/grad_norm': 0.6542133092880249, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:51:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:51:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:04] train_sft.py:596 - step 841: {'train/loss': 0.10056686401367188, 'train/grad_norm': 0.6504274606704712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 12:52:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:52:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:09] train_sft.py:596 - step 842: {'train/loss': 0.07713720202445984, 'train/grad_norm': 0.756752610206604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:52:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:52:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:14] train_sft.py:596 - step 843: {'train/loss': 0.1002054214477539, 'train/grad_norm': 0.8256952166557312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 12:52:14] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:52:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:19] train_sft.py:596 - step 844: {'train/loss': 0.0902099609375, 'train/grad_norm': 0.8811492919921875, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:52:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:52:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:23] train_sft.py:596 - step 845: {'train/loss': 0.10092353820800781, 'train/grad_norm': 0.9989103078842163, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:52:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:52:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:28] train_sft.py:596 - step 846: {'train/loss': 0.10611343383789062, 'train/grad_norm': 0.7569880485534668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 12:52:28] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:52:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:33] train_sft.py:596 - step 847: {'train/loss': 0.10405433177947998, 'train/grad_norm': 1.1650680303573608, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:52:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:52:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:39] train_sft.py:596 - step 848: {'train/loss': 0.10355377197265625, 'train/grad_norm': 0.6947790384292603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.33s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.33s/it]
[2025-12-29 12:52:39] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 12:52:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:43] train_sft.py:596 - step 849: {'train/loss': 0.11749458312988281, 'train/grad_norm': 0.7577227354049683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 12:52:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:52:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:48] train_sft.py:596 - step 850: {'train/loss': 0.07595443725585938, 'train/grad_norm': 0.741237461566925, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 12:52:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:52:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:53] train_sft.py:596 - step 851: {'train/loss': 0.10101318359375, 'train/grad_norm': 1.070560336112976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:52:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:52:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:52:58] train_sft.py:596 - step 852: {'train/loss': 0.10775876045227051, 'train/grad_norm': 1.3717256784439087, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 12:52:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:52:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:03] train_sft.py:596 - step 853: {'train/loss': 0.09676361083984375, 'train/grad_norm': 0.831693708896637, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:53:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:53:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:07] train_sft.py:596 - step 854: {'train/loss': 0.09150004386901855, 'train/grad_norm': 0.7296031713485718, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 12:53:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:53:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:12] train_sft.py:596 - step 855: {'train/loss': 0.08089065551757812, 'train/grad_norm': 0.6613234877586365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 12:53:12] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:53:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:17] train_sft.py:596 - step 856: {'train/loss': 0.09063959121704102, 'train/grad_norm': 0.7156380414962769, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:53:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:53:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:22] train_sft.py:596 - step 857: {'train/loss': 0.08146953582763672, 'train/grad_norm': 0.9123225212097168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 12:53:22] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:53:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:27] train_sft.py:596 - step 858: {'train/loss': 0.08749103546142578, 'train/grad_norm': 0.7341001033782959, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:53:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:53:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:32] train_sft.py:596 - step 859: {'train/loss': 0.09686660766601562, 'train/grad_norm': 0.7446978092193604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 12:53:32] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:53:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:37] train_sft.py:596 - step 860: {'train/loss': 0.100006103515625, 'train/grad_norm': 0.8126304149627686, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 12:53:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:53:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:43] train_sft.py:596 - step 861: {'train/loss': 0.10898208618164062, 'train/grad_norm': 0.7264509797096252, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 12:53:43] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:53:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:47] train_sft.py:596 - step 862: {'train/loss': 0.10462188720703125, 'train/grad_norm': 3.0893542766571045, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:53:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:53:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:52] train_sft.py:596 - step 863: {'train/loss': 0.10573196411132812, 'train/grad_norm': 0.7959001660346985, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:53:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:53:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:53:57] train_sft.py:596 - step 864: {'train/loss': 0.08748537302017212, 'train/grad_norm': 0.882952868938446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:53:57] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:53:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:02] train_sft.py:596 - step 865: {'train/loss': 0.0897665023803711, 'train/grad_norm': 0.7035475373268127, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:54:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:54:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:06] train_sft.py:596 - step 866: {'train/loss': 0.11707592010498047, 'train/grad_norm': 1.0246002674102783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:54:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:54:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:11] train_sft.py:596 - step 867: {'train/loss': 0.09498214721679688, 'train/grad_norm': 0.7457023859024048, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:54:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:54:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:16] train_sft.py:596 - step 868: {'train/loss': 0.11488533020019531, 'train/grad_norm': 1.0703073740005493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:54:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:54:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:21] train_sft.py:596 - step 869: {'train/loss': 0.1163642406463623, 'train/grad_norm': 0.5989470481872559, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:54:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:54:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:26] train_sft.py:596 - step 870: {'train/loss': 0.09655261039733887, 'train/grad_norm': 0.8925093412399292, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:54:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:54:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:31] train_sft.py:596 - step 871: {'train/loss': 0.09396171569824219, 'train/grad_norm': 0.5145799517631531, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 12:54:31] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 12:54:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:36] train_sft.py:596 - step 872: {'train/loss': 0.12140274047851562, 'train/grad_norm': 1.0640578269958496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 12:54:36] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 12:54:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:41] train_sft.py:596 - step 873: {'train/loss': 0.10248184204101562, 'train/grad_norm': 0.5686591267585754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:54:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:54:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:46] train_sft.py:596 - step 874: {'train/loss': 0.0914754867553711, 'train/grad_norm': 0.5768665075302124, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 12:54:46] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:54:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:51] train_sft.py:596 - step 875: {'train/loss': 0.09342694282531738, 'train/grad_norm': 0.8458963632583618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 12:54:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:54:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:54:56] train_sft.py:596 - step 876: {'train/loss': 0.1027984619140625, 'train/grad_norm': 0.7172425389289856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:54:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:54:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:02] train_sft.py:596 - step 877: {'train/loss': 0.09776592254638672, 'train/grad_norm': 0.7222979068756104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.31s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.31s/it]
[2025-12-29 12:55:02] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 12:55:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:07] train_sft.py:596 - step 878: {'train/loss': 0.09288787841796875, 'train/grad_norm': 0.886343777179718, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
[2025-12-29 12:55:07] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 12:55:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:12] train_sft.py:596 - step 879: {'train/loss': 0.09963822364807129, 'train/grad_norm': 0.7711767554283142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
[2025-12-29 12:55:12] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 12:55:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:17] train_sft.py:596 - step 880: {'train/loss': 0.08846282958984375, 'train/grad_norm': 0.6609867215156555, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 12:55:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:55:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:22] train_sft.py:596 - step 881: {'train/loss': 0.10770893096923828, 'train/grad_norm': 0.8720365166664124, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 12:55:22] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 12:55:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:27] train_sft.py:596 - step 882: {'train/loss': 0.09312057495117188, 'train/grad_norm': 0.6344590783119202, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 12:55:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:55:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:32] train_sft.py:596 - step 883: {'train/loss': 0.09362220764160156, 'train/grad_norm': 0.7127280831336975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 12:55:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:55:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:37] train_sft.py:596 - step 884: {'train/loss': 0.10736083984375, 'train/grad_norm': 0.758303701877594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:55:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:55:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:42] train_sft.py:596 - step 885: {'train/loss': 0.0869131088256836, 'train/grad_norm': 0.7028966546058655, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:55:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:55:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:46] train_sft.py:596 - step 886: {'train/loss': 0.08758354187011719, 'train/grad_norm': 0.595313549041748, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 12:55:46] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:55:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:51] train_sft.py:596 - step 887: {'train/loss': 0.1152353286743164, 'train/grad_norm': 0.7306125164031982, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 12:55:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:55:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:55:57] train_sft.py:596 - step 888: {'train/loss': 0.08330154418945312, 'train/grad_norm': 1.5188393592834473, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 12:55:57] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 12:55:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:01] train_sft.py:596 - step 889: {'train/loss': 0.10172200202941895, 'train/grad_norm': 0.6091790795326233, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:56:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:56:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:06] train_sft.py:596 - step 890: {'train/loss': 0.10244083404541016, 'train/grad_norm': 0.8928064107894897, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:56:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:56:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:11] train_sft.py:596 - step 891: {'train/loss': 0.10019969940185547, 'train/grad_norm': 0.5946300625801086, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:56:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:56:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:16] train_sft.py:596 - step 892: {'train/loss': 0.1059722900390625, 'train/grad_norm': 0.8453903198242188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 12:56:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:56:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:20] train_sft.py:596 - step 893: {'train/loss': 0.09070372581481934, 'train/grad_norm': 0.5953763127326965, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:56:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:56:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:25] train_sft.py:596 - step 894: {'train/loss': 0.10401344299316406, 'train/grad_norm': 0.5918213725090027, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 12:56:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:56:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:30] train_sft.py:596 - step 895: {'train/loss': 0.08107185363769531, 'train/grad_norm': 1.1158368587493896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 12:56:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:56:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:35] train_sft.py:596 - step 896: {'train/loss': 0.10529851913452148, 'train/grad_norm': 1.0249007940292358, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 12:56:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:56:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:40] train_sft.py:596 - step 897: {'train/loss': 0.11878347396850586, 'train/grad_norm': 0.8926343321800232, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:56:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:56:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:45] train_sft.py:596 - step 898: {'train/loss': 0.10466766357421875, 'train/grad_norm': 0.7249667644500732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 12:56:45] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:56:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:50] train_sft.py:596 - step 899: {'train/loss': 0.09995603561401367, 'train/grad_norm': 0.8404598236083984, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:56:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:56:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:54] train_sft.py:596 - step 900: {'train/loss': 0.1001119613647461, 'train/grad_norm': 0.6594532132148743, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:56:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:56:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:56:59] train_sft.py:596 - step 901: {'train/loss': 0.06868407130241394, 'train/grad_norm': 0.6225162744522095, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 12:56:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:56:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:04] train_sft.py:596 - step 902: {'train/loss': 0.07351112365722656, 'train/grad_norm': 0.5879145264625549, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:57:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:57:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:08] train_sft.py:596 - step 903: {'train/loss': 0.05056774616241455, 'train/grad_norm': 0.5662115812301636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 12:57:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:57:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:13] train_sft.py:596 - step 904: {'train/loss': 0.06899261474609375, 'train/grad_norm': 0.7901020646095276, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 12:57:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:57:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:18] train_sft.py:596 - step 905: {'train/loss': 0.05438232421875, 'train/grad_norm': 0.8136578798294067, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 12:57:18] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:57:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:22] train_sft.py:596 - step 906: {'train/loss': 0.061946868896484375, 'train/grad_norm': 0.8156537413597107, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 12:57:22] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:57:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:27] train_sft.py:596 - step 907: {'train/loss': 0.04866921901702881, 'train/grad_norm': 0.4798718988895416, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 12:57:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:57:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:32] train_sft.py:596 - step 908: {'train/loss': 0.052197933197021484, 'train/grad_norm': 0.557796835899353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 12:57:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 12:57:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:37] train_sft.py:596 - step 909: {'train/loss': 0.05000925064086914, 'train/grad_norm': 0.5559725761413574, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:57:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:57:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:42] train_sft.py:596 - step 910: {'train/loss': 0.062384724617004395, 'train/grad_norm': 0.7028079032897949, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 12:57:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 12:57:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:47] train_sft.py:596 - step 911: {'train/loss': 0.053864479064941406, 'train/grad_norm': 0.9431789517402649, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:57:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:57:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:51] train_sft.py:596 - step 912: {'train/loss': 0.04976201057434082, 'train/grad_norm': 0.7893761992454529, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:57:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:57:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:57:56] train_sft.py:596 - step 913: {'train/loss': 0.05414104461669922, 'train/grad_norm': 0.7898350358009338, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:57:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:57:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:00] train_sft.py:596 - step 914: {'train/loss': 0.06118084490299225, 'train/grad_norm': 0.9819661974906921, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 12:58:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:58:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:05] train_sft.py:596 - step 915: {'train/loss': 0.031190156936645508, 'train/grad_norm': 0.560092568397522, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 12:58:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:10] train_sft.py:596 - step 916: {'train/loss': 0.0464911162853241, 'train/grad_norm': 1.029057264328003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:58:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:14] train_sft.py:596 - step 917: {'train/loss': 0.05726134032011032, 'train/grad_norm': 0.7577219605445862, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:58:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:19] train_sft.py:596 - step 918: {'train/loss': 0.06865692138671875, 'train/grad_norm': 1.0993146896362305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:58:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:24] train_sft.py:596 - step 919: {'train/loss': 0.05092760920524597, 'train/grad_norm': 0.8874340653419495, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 12:58:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:28] train_sft.py:596 - step 920: {'train/loss': 0.05734062194824219, 'train/grad_norm': 0.8223116993904114, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 12:58:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:33] train_sft.py:596 - step 921: {'train/loss': 0.043677330017089844, 'train/grad_norm': 0.7513076066970825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 12:58:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:38] train_sft.py:596 - step 922: {'train/loss': 0.05771994590759277, 'train/grad_norm': 0.7065180540084839, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:58:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:43] train_sft.py:596 - step 923: {'train/loss': 0.05886119604110718, 'train/grad_norm': 0.8763899803161621, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 12:58:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:47] train_sft.py:596 - step 924: {'train/loss': 0.06818950176239014, 'train/grad_norm': 0.7181344032287598, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:58:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:58:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:52] train_sft.py:596 - step 925: {'train/loss': 0.06952905654907227, 'train/grad_norm': 0.589857280254364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 12:58:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:58:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:58:57] train_sft.py:596 - step 926: {'train/loss': 0.05531573295593262, 'train/grad_norm': 0.6798864006996155, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:58:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:58:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:01] train_sft.py:596 - step 927: {'train/loss': 0.049546003341674805, 'train/grad_norm': 0.6506755948066711, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 12:59:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 12:59:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:06] train_sft.py:596 - step 928: {'train/loss': 0.05210292339324951, 'train/grad_norm': 0.6798309683799744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 12:59:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:11] train_sft.py:596 - step 929: {'train/loss': 0.07091808319091797, 'train/grad_norm': 1.0089168548583984, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 12:59:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:15] train_sft.py:596 - step 930: {'train/loss': 0.05793285369873047, 'train/grad_norm': 0.7030149698257446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:59:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:20] train_sft.py:596 - step 931: {'train/loss': 0.05927693843841553, 'train/grad_norm': 0.7748365998268127, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:59:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:25] train_sft.py:596 - step 932: {'train/loss': 0.04781770706176758, 'train/grad_norm': 0.5688628554344177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 12:59:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:29] train_sft.py:596 - step 933: {'train/loss': 0.060366153717041016, 'train/grad_norm': 0.8095594048500061, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:59:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:34] train_sft.py:596 - step 934: {'train/loss': 0.07315635681152344, 'train/grad_norm': 0.7807851433753967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 12:59:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:38] train_sft.py:596 - step 935: {'train/loss': 0.05495095252990723, 'train/grad_norm': 0.8269230127334595, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 12:59:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:59:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:43] train_sft.py:596 - step 936: {'train/loss': 0.05401802062988281, 'train/grad_norm': 0.653301477432251, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 12:59:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 12:59:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:48] train_sft.py:596 - step 937: {'train/loss': 0.062294840812683105, 'train/grad_norm': 0.9306644797325134, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 12:59:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:59:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:52] train_sft.py:596 - step 938: {'train/loss': 0.06821155548095703, 'train/grad_norm': 1.0304756164550781, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 12:59:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:59:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 12:59:57] train_sft.py:596 - step 939: {'train/loss': 0.0596923828125, 'train/grad_norm': 0.6799522042274475, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 12:59:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 12:59:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:02] train_sft.py:596 - step 940: {'train/loss': 0.06604915857315063, 'train/grad_norm': 0.8792475461959839, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:00:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:00:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:06] train_sft.py:596 - step 941: {'train/loss': 0.05177879333496094, 'train/grad_norm': 0.8692612648010254, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:00:06] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:00:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:11] train_sft.py:596 - step 942: {'train/loss': 0.0848226547241211, 'train/grad_norm': 0.9171664714813232, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:00:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:00:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:16] train_sft.py:596 - step 943: {'train/loss': 0.06256532669067383, 'train/grad_norm': 0.6861428618431091, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:00:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:00:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:20] train_sft.py:596 - step 944: {'train/loss': 0.05889749526977539, 'train/grad_norm': 0.7502537369728088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:00:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:00:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:25] train_sft.py:596 - step 945: {'train/loss': 0.0647592544555664, 'train/grad_norm': 0.7650294899940491, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:00:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:00:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:30] train_sft.py:596 - step 946: {'train/loss': 0.07353973388671875, 'train/grad_norm': 0.8811075091362, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:00:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:00:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:34] train_sft.py:596 - step 947: {'train/loss': 0.05775421857833862, 'train/grad_norm': 0.6526618599891663, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:00:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:00:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:39] train_sft.py:596 - step 948: {'train/loss': 0.05795478820800781, 'train/grad_norm': 0.5647172927856445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 13:00:39] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:00:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:44] train_sft.py:596 - step 949: {'train/loss': 0.04644918441772461, 'train/grad_norm': 0.6050310730934143, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:00:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:00:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:48] train_sft.py:596 - step 950: {'train/loss': 0.06081056594848633, 'train/grad_norm': 0.6407104134559631, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:00:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:00:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:53] train_sft.py:596 - step 951: {'train/loss': 0.05580735206604004, 'train/grad_norm': 0.8678750991821289, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:00:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:00:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:00:58] train_sft.py:596 - step 952: {'train/loss': 0.055329084396362305, 'train/grad_norm': 0.5909973978996277, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:00:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:00:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:02] train_sft.py:596 - step 953: {'train/loss': 0.06653505563735962, 'train/grad_norm': 0.625234067440033, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:01:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:07] train_sft.py:596 - step 954: {'train/loss': 0.06481122970581055, 'train/grad_norm': 0.8778198957443237, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:01:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:01:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:12] train_sft.py:596 - step 955: {'train/loss': 0.06137925386428833, 'train/grad_norm': 0.84037846326828, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:01:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:16] train_sft.py:596 - step 956: {'train/loss': 0.048769235610961914, 'train/grad_norm': 0.8642420768737793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:01:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:21] train_sft.py:596 - step 957: {'train/loss': 0.05244755744934082, 'train/grad_norm': 0.7014748454093933, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:01:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:01:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:26] train_sft.py:596 - step 958: {'train/loss': 0.061652421951293945, 'train/grad_norm': 0.9781976342201233, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:01:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:01:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:30] train_sft.py:596 - step 959: {'train/loss': 0.06120896339416504, 'train/grad_norm': 0.7413657307624817, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:01:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:35] train_sft.py:596 - step 960: {'train/loss': 0.07634925842285156, 'train/grad_norm': 1.0925488471984863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:01:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:01:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:40] train_sft.py:596 - step 961: {'train/loss': 0.058719635009765625, 'train/grad_norm': 0.7199335098266602, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:01:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:44] train_sft.py:596 - step 962: {'train/loss': 0.0566178560256958, 'train/grad_norm': 0.8047907948493958, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:01:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:49] train_sft.py:596 - step 963: {'train/loss': 0.053320229053497314, 'train/grad_norm': 0.5877443552017212, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:01:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:01:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:54] train_sft.py:596 - step 964: {'train/loss': 0.06446897983551025, 'train/grad_norm': 0.7011105418205261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:01:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:01:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:01:58] train_sft.py:596 - step 965: {'train/loss': 0.07503142952919006, 'train/grad_norm': 1.1768935918807983, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:01:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:01:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:03] train_sft.py:596 - step 966: {'train/loss': 0.06589806079864502, 'train/grad_norm': 0.6426215171813965, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:02:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:08] train_sft.py:596 - step 967: {'train/loss': 0.0459478497505188, 'train/grad_norm': 0.5525194406509399, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:02:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:12] train_sft.py:596 - step 968: {'train/loss': 0.05755758285522461, 'train/grad_norm': 0.8014693856239319, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:02:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:17] train_sft.py:596 - step 969: {'train/loss': 0.06671977043151855, 'train/grad_norm': 0.751429557800293, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:02:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:21] train_sft.py:596 - step 970: {'train/loss': 0.05975764989852905, 'train/grad_norm': 0.9046117663383484, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:02:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:26] train_sft.py:596 - step 971: {'train/loss': 0.06065082550048828, 'train/grad_norm': 0.5824262499809265, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:02:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:31] train_sft.py:596 - step 972: {'train/loss': 0.06123971939086914, 'train/grad_norm': 0.6156788468360901, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:02:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:35] train_sft.py:596 - step 973: {'train/loss': 0.06515693664550781, 'train/grad_norm': 0.6508114337921143, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:02:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:02:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:40] train_sft.py:596 - step 974: {'train/loss': 0.0629732608795166, 'train/grad_norm': 0.6882249712944031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 13:02:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:02:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:45] train_sft.py:596 - step 975: {'train/loss': 0.05601465702056885, 'train/grad_norm': 0.8358780145645142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:02:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:49] train_sft.py:596 - step 976: {'train/loss': 0.056565284729003906, 'train/grad_norm': 0.800180971622467, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:02:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:54] train_sft.py:596 - step 977: {'train/loss': 0.05999326705932617, 'train/grad_norm': 0.8094218969345093, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:02:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:02:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:02:59] train_sft.py:596 - step 978: {'train/loss': 0.05122244358062744, 'train/grad_norm': 0.5464489459991455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:02:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:02:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:04] train_sft.py:596 - step 979: {'train/loss': 0.06275582313537598, 'train/grad_norm': 0.6905966997146606, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:03:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:03:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:08] train_sft.py:596 - step 980: {'train/loss': 0.05433160066604614, 'train/grad_norm': 0.7655078172683716, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:03:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:03:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:13] train_sft.py:596 - step 981: {'train/loss': 0.07843589782714844, 'train/grad_norm': 0.7249224781990051, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:03:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:03:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:18] train_sft.py:596 - step 982: {'train/loss': 0.07340812683105469, 'train/grad_norm': 0.8559375405311584, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:03:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:03:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:22] train_sft.py:596 - step 983: {'train/loss': 0.04975080490112305, 'train/grad_norm': 1.158618688583374, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:03:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:03:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:27] train_sft.py:596 - step 984: {'train/loss': 0.0479658842086792, 'train/grad_norm': 0.8242771625518799, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:03:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:03:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:32] train_sft.py:596 - step 985: {'train/loss': 0.0776987075805664, 'train/grad_norm': 1.111857295036316, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:03:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:03:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:36] train_sft.py:596 - step 986: {'train/loss': 0.0617218017578125, 'train/grad_norm': 0.6788593530654907, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:03:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:03:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:41] train_sft.py:596 - step 987: {'train/loss': 0.06141805648803711, 'train/grad_norm': 0.7275349497795105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:03:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:03:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:46] train_sft.py:596 - step 988: {'train/loss': 0.06383633613586426, 'train/grad_norm': 0.6391281485557556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:03:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:03:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:50] train_sft.py:596 - step 989: {'train/loss': 0.10148906707763672, 'train/grad_norm': 0.795322597026825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:03:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:03:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:03:55] train_sft.py:596 - step 990: {'train/loss': 0.06276494264602661, 'train/grad_norm': 0.9340419769287109, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 13:03:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:03:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:00] train_sft.py:596 - step 991: {'train/loss': 0.07069015502929688, 'train/grad_norm': 0.9103236198425293, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:04:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:04:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:04] train_sft.py:596 - step 992: {'train/loss': 0.0674041211605072, 'train/grad_norm': 0.6146971583366394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:04:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:04:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:09] train_sft.py:596 - step 993: {'train/loss': 0.07951261103153229, 'train/grad_norm': 0.8773024082183838, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:04:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:04:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:14] train_sft.py:596 - step 994: {'train/loss': 0.05070316791534424, 'train/grad_norm': 0.49169275164604187, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:04:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:04:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:19] train_sft.py:596 - step 995: {'train/loss': 0.07458019256591797, 'train/grad_norm': 0.8683161735534668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:04:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:04:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:24] train_sft.py:596 - step 996: {'train/loss': 0.05933427810668945, 'train/grad_norm': 0.7588886022567749, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.33s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.33s/it]
[2025-12-29 13:04:24] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 13:04:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:29] train_sft.py:596 - step 997: {'train/loss': 0.04764437675476074, 'train/grad_norm': 0.8663657903671265, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:04:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:04:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:34] train_sft.py:596 - step 998: {'train/loss': 0.05547812581062317, 'train/grad_norm': 0.8658741116523743, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:04:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:04:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:04:39] train_sft.py:596 - step 999: {'train/loss': 0.05130147933959961, 'train/grad_norm': 0.6167904734611511, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:04:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:04:51] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0001000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:03<05:14,  3.18s/it]
actor_val:   2%|▏         | 2/100 [00:04<03:40,  2.25s/it]
actor_val:   3%|▎         | 3/100 [00:06<03:22,  2.09s/it]
actor_val:   4%|▍         | 4/100 [00:08<03:01,  1.89s/it]
actor_val:   5%|▌         | 5/100 [00:09<02:48,  1.77s/it]
actor_val:   6%|▌         | 6/100 [00:11<02:42,  1.72s/it]
actor_val:   7%|▋         | 7/100 [00:12<02:34,  1.66s/it]
actor_val:   8%|▊         | 8/100 [00:14<02:29,  1.63s/it]
actor_val:   9%|▉         | 9/100 [00:16<02:26,  1.60s/it]
actor_val:  10%|█         | 10/100 [00:17<02:24,  1.60s/it]
actor_val:  11%|█         | 11/100 [00:19<02:22,  1.60s/it]
actor_val:  12%|█▏        | 12/100 [00:20<02:19,  1.58s/it]
actor_val:  13%|█▎        | 13/100 [00:22<02:23,  1.65s/it]
actor_val:  14%|█▍        | 14/100 [00:24<02:27,  1.72s/it]
actor_val:  15%|█▌        | 15/100 [00:26<02:21,  1.66s/it]
actor_val:  16%|█▌        | 16/100 [00:27<02:17,  1.64s/it]
actor_val:  17%|█▋        | 17/100 [00:29<02:14,  1.62s/it]
actor_val:  18%|█▊        | 18/100 [00:30<02:11,  1.60s/it]
actor_val:  19%|█▉        | 19/100 [00:32<02:08,  1.58s/it]
actor_val:  20%|██        | 20/100 [00:34<02:13,  1.67s/it]
actor_val:  21%|██        | 21/100 [00:35<02:14,  1.71s/it]
actor_val:  22%|██▏       | 22/100 [00:37<02:11,  1.68s/it]
actor_val:  23%|██▎       | 23/100 [00:39<02:06,  1.64s/it]
actor_val:  24%|██▍       | 24/100 [00:40<02:02,  1.61s/it]
actor_val:  25%|██▌       | 25/100 [00:42<01:59,  1.60s/it]
actor_val:  26%|██▌       | 26/100 [00:44<02:06,  1.70s/it]
actor_val:  27%|██▋       | 27/100 [00:45<02:00,  1.65s/it]
actor_val:  28%|██▊       | 28/100 [00:47<02:02,  1.70s/it]
actor_val:  29%|██▉       | 29/100 [00:49<01:57,  1.66s/it]
actor_val:  30%|███       | 30/100 [00:50<01:54,  1.63s/it]
actor_val:  31%|███       | 31/100 [00:52<01:51,  1.61s/it]
actor_val:  32%|███▏      | 32/100 [00:53<01:49,  1.61s/it]
actor_val:  33%|███▎      | 33/100 [00:55<01:47,  1.60s/it]
actor_val:  34%|███▍      | 34/100 [00:56<01:45,  1.59s/it]
actor_val:  35%|███▌      | 35/100 [00:58<01:42,  1.58s/it]
actor_val:  36%|███▌      | 36/100 [01:00<01:40,  1.58s/it]
actor_val:  37%|███▋      | 37/100 [01:01<01:39,  1.57s/it]
actor_val:  38%|███▊      | 38/100 [01:03<01:38,  1.58s/it]
actor_val:  39%|███▉      | 39/100 [01:04<01:36,  1.58s/it]
actor_val:  40%|████      | 40/100 [01:06<01:34,  1.57s/it]
actor_val:  41%|████      | 41/100 [01:07<01:32,  1.56s/it]
actor_val:  42%|████▏     | 42/100 [01:09<01:34,  1.64s/it]
actor_val:  43%|████▎     | 43/100 [01:11<01:37,  1.71s/it]
actor_val:  44%|████▍     | 44/100 [01:13<01:38,  1.75s/it]
actor_val:  45%|████▌     | 45/100 [01:15<01:33,  1.70s/it]
actor_val:  46%|████▌     | 46/100 [01:16<01:33,  1.74s/it]
actor_val:  47%|████▋     | 47/100 [01:18<01:33,  1.77s/it]
actor_val:  48%|████▊     | 48/100 [01:20<01:33,  1.81s/it]
actor_val:  49%|████▉     | 49/100 [01:22<01:27,  1.72s/it]
actor_val:  50%|█████     | 50/100 [01:23<01:23,  1.67s/it]
actor_val:  51%|█████     | 51/100 [01:25<01:20,  1.64s/it]
actor_val:  52%|█████▏    | 52/100 [01:27<01:22,  1.71s/it]
actor_val:  53%|█████▎    | 53/100 [01:28<01:18,  1.67s/it]
actor_val:  54%|█████▍    | 54/100 [01:30<01:19,  1.73s/it]
actor_val:  55%|█████▌    | 55/100 [01:32<01:19,  1.77s/it]
actor_val:  56%|█████▌    | 56/100 [01:34<01:18,  1.79s/it]
actor_val:  57%|█████▋    | 57/100 [01:35<01:13,  1.71s/it]
actor_val:  58%|█████▊    | 58/100 [01:37<01:14,  1.77s/it]
actor_val:  59%|█████▉    | 59/100 [01:39<01:09,  1.71s/it]
actor_val:  60%|██████    | 60/100 [01:40<01:06,  1.66s/it]
actor_val:  61%|██████    | 61/100 [01:42<01:06,  1.72s/it]
actor_val:  62%|██████▏   | 62/100 [01:44<01:03,  1.68s/it]
actor_val:  63%|██████▎   | 63/100 [01:45<01:00,  1.63s/it]
actor_val:  64%|██████▍   | 64/100 [01:47<01:00,  1.69s/it]
actor_val:  65%|██████▌   | 65/100 [01:49<00:57,  1.65s/it]
actor_val:  66%|██████▌   | 66/100 [01:50<00:57,  1.70s/it]
actor_val:  67%|██████▋   | 67/100 [01:52<00:55,  1.68s/it]
actor_val:  68%|██████▊   | 68/100 [01:54<00:55,  1.72s/it]
actor_val:  69%|██████▉   | 69/100 [01:55<00:51,  1.67s/it]
actor_val:  70%|███████   | 70/100 [01:57<00:52,  1.75s/it]
actor_val:  71%|███████   | 71/100 [01:59<00:48,  1.69s/it]
actor_val:  72%|███████▏  | 72/100 [02:01<00:46,  1.66s/it]
actor_val:  73%|███████▎  | 73/100 [02:02<00:43,  1.63s/it]
actor_val:  74%|███████▍  | 74/100 [02:04<00:41,  1.61s/it]
actor_val:  75%|███████▌  | 75/100 [02:05<00:41,  1.65s/it]
actor_val:  76%|███████▌  | 76/100 [02:07<00:41,  1.73s/it]
actor_val:  77%|███████▋  | 77/100 [02:09<00:38,  1.68s/it]
actor_val:  78%|███████▊  | 78/100 [02:10<00:36,  1.65s/it]
actor_val:  79%|███████▉  | 79/100 [02:12<00:33,  1.62s/it]
actor_val:  80%|████████  | 80/100 [02:14<00:32,  1.60s/it]
actor_val:  81%|████████  | 81/100 [02:15<00:30,  1.59s/it]
actor_val:  82%|████████▏ | 82/100 [02:17<00:29,  1.66s/it]
actor_val:  83%|████████▎ | 83/100 [02:19<00:29,  1.73s/it]
actor_val:  84%|████████▍ | 84/100 [02:21<00:27,  1.75s/it]
actor_val:  85%|████████▌ | 85/100 [02:23<00:26,  1.78s/it]
actor_val:  86%|████████▌ | 86/100 [02:24<00:23,  1.71s/it]
actor_val:  87%|████████▋ | 87/100 [02:26<00:21,  1.67s/it]
actor_val:  88%|████████▊ | 88/100 [02:27<00:20,  1.73s/it]
actor_val:  89%|████████▉ | 89/100 [02:29<00:18,  1.67s/it]
actor_val:  90%|█████████ | 90/100 [02:31<00:16,  1.63s/it]
actor_val:  91%|█████████ | 91/100 [02:32<00:15,  1.70s/it]
actor_val:  92%|█████████▏| 92/100 [02:34<00:13,  1.73s/it]
actor_val:  93%|█████████▎| 93/100 [02:36<00:11,  1.69s/it]
actor_val:  94%|█████████▍| 94/100 [02:38<00:10,  1.76s/it]
actor_val:  95%|█████████▌| 95/100 [02:40<00:08,  1.78s/it]
actor_val:  96%|█████████▌| 96/100 [02:41<00:06,  1.72s/it]
actor_val:  97%|█████████▋| 97/100 [02:43<00:05,  1.68s/it]
actor_val:  98%|█████████▊| 98/100 [02:45<00:03,  1.74s/it]
actor_val:  99%|█████████▉| 99/100 [02:46<00:01,  1.77s/it]
actor_val: 100%|██████████| 100/100 [02:48<00:00,  1.72s/it]
actor_val: 100%|██████████| 100/100 [02:48<00:00,  1.69s/it]
[2025-12-29 13:07:39] train_sft.py:658 - step 1000: {'val/loss': 0.3264453125}
[2025-12-29 13:07:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:07:45] train_sft.py:596 - step 1000: {'train/loss': 0.06721436977386475, 'train/grad_norm': 0.9944223165512085, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 13:07:45] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:07:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:07:50] train_sft.py:596 - step 1001: {'train/loss': 0.07139754295349121, 'train/grad_norm': 1.2949504852294922, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:07:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:07:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:07:54] train_sft.py:596 - step 1002: {'train/loss': 0.07476425170898438, 'train/grad_norm': 0.7844398617744446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:07:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:07:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:07:59] train_sft.py:596 - step 1003: {'train/loss': 0.07404708862304688, 'train/grad_norm': 0.9899093508720398, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:07:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:08:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:04] train_sft.py:596 - step 1004: {'train/loss': 0.05295109748840332, 'train/grad_norm': 0.6052682995796204, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:08:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:08:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:09] train_sft.py:596 - step 1005: {'train/loss': 0.05532693862915039, 'train/grad_norm': 0.5161461234092712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:08:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:08:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:13] train_sft.py:596 - step 1006: {'train/loss': 0.0569300651550293, 'train/grad_norm': 0.5587820410728455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:08:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:08:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:18] train_sft.py:596 - step 1007: {'train/loss': 0.07977294921875, 'train/grad_norm': 0.9568223357200623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:08:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:08:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:23] train_sft.py:596 - step 1008: {'train/loss': 0.06130576133728027, 'train/grad_norm': 0.569252073764801, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:08:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:08:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:28] train_sft.py:596 - step 1009: {'train/loss': 0.05625614523887634, 'train/grad_norm': 0.487946480512619, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:08:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:08:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:33] train_sft.py:596 - step 1010: {'train/loss': 0.050823211669921875, 'train/grad_norm': 0.472454309463501, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 13:08:33] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:08:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:38] train_sft.py:596 - step 1011: {'train/loss': 0.05857658386230469, 'train/grad_norm': 0.6169377565383911, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 13:08:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:08:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:43] train_sft.py:596 - step 1012: {'train/loss': 0.0702967643737793, 'train/grad_norm': 1.010878562927246, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:08:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:08:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:47] train_sft.py:596 - step 1013: {'train/loss': 0.060898780822753906, 'train/grad_norm': 0.8768392205238342, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:08:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:08:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:53] train_sft.py:596 - step 1014: {'train/loss': 0.061702728271484375, 'train/grad_norm': 0.7003921866416931, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 13:08:53] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:08:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:08:57] train_sft.py:596 - step 1015: {'train/loss': 0.04578065872192383, 'train/grad_norm': 0.5976152420043945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:08:57] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:08:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:03] train_sft.py:596 - step 1016: {'train/loss': 0.05790233612060547, 'train/grad_norm': 0.9273791313171387, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 13:09:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:09:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:08] train_sft.py:596 - step 1017: {'train/loss': 0.045752644538879395, 'train/grad_norm': 0.70139479637146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:09:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:09:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:12] train_sft.py:596 - step 1018: {'train/loss': 0.07353609800338745, 'train/grad_norm': 0.8017830848693848, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:09:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:09:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:17] train_sft.py:596 - step 1019: {'train/loss': 0.05411207675933838, 'train/grad_norm': 0.6481944918632507, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:09:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:09:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:22] train_sft.py:596 - step 1020: {'train/loss': 0.07094332203269005, 'train/grad_norm': 0.7966929078102112, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:09:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:09:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:26] train_sft.py:596 - step 1021: {'train/loss': 0.05930197238922119, 'train/grad_norm': 0.8235781192779541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:09:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:09:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:31] train_sft.py:596 - step 1022: {'train/loss': 0.07093620300292969, 'train/grad_norm': 0.6486759185791016, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:09:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:09:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:36] train_sft.py:596 - step 1023: {'train/loss': 0.07172012329101562, 'train/grad_norm': 0.7403872609138489, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 13:09:36] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:09:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:41] train_sft.py:596 - step 1024: {'train/loss': 0.0890575647354126, 'train/grad_norm': 0.6913124918937683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:09:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:09:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:46] train_sft.py:596 - step 1025: {'train/loss': 0.06746873259544373, 'train/grad_norm': 1.0452227592468262, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 13:09:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:09:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:51] train_sft.py:596 - step 1026: {'train/loss': 0.07430338859558105, 'train/grad_norm': 1.8103171586990356, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 13:09:51] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:09:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:09:55] train_sft.py:596 - step 1027: {'train/loss': 0.05786490440368652, 'train/grad_norm': 1.148199439048767, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:09:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:09:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:00] train_sft.py:596 - step 1028: {'train/loss': 0.06427955627441406, 'train/grad_norm': 0.577673077583313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:10:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:10:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:05] train_sft.py:596 - step 1029: {'train/loss': 0.061621665954589844, 'train/grad_norm': 0.6932931542396545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:10:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:10:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:10] train_sft.py:596 - step 1030: {'train/loss': 0.05251026153564453, 'train/grad_norm': 0.6232039928436279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 13:10:10] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:10:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:15] train_sft.py:596 - step 1031: {'train/loss': 0.04744076728820801, 'train/grad_norm': 0.5256818532943726, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:10:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:10:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:20] train_sft.py:596 - step 1032: {'train/loss': 0.06312191486358643, 'train/grad_norm': 0.6883330345153809, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 13:10:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:10:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:25] train_sft.py:596 - step 1033: {'train/loss': 0.07021903991699219, 'train/grad_norm': 0.8762761354446411, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:10:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:10:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:29] train_sft.py:596 - step 1034: {'train/loss': 0.06886720657348633, 'train/grad_norm': 0.8063328266143799, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:10:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:10:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:34] train_sft.py:596 - step 1035: {'train/loss': 0.0729522705078125, 'train/grad_norm': 0.6377546191215515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:10:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:10:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:39] train_sft.py:596 - step 1036: {'train/loss': 0.06340551376342773, 'train/grad_norm': 0.7370354533195496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:10:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:10:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:43] train_sft.py:596 - step 1037: {'train/loss': 0.04708617925643921, 'train/grad_norm': 0.81368088722229, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:10:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:10:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:48] train_sft.py:596 - step 1038: {'train/loss': 0.061812639236450195, 'train/grad_norm': 0.5092300772666931, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:10:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:10:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:53] train_sft.py:596 - step 1039: {'train/loss': 0.06396859884262085, 'train/grad_norm': 0.8790097236633301, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:10:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:10:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:10:58] train_sft.py:596 - step 1040: {'train/loss': 0.08396530151367188, 'train/grad_norm': 0.9212937951087952, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:10:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:10:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:03] train_sft.py:596 - step 1041: {'train/loss': 0.048432350158691406, 'train/grad_norm': 0.9982482194900513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 13:11:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:11:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:08] train_sft.py:596 - step 1042: {'train/loss': 0.05917549133300781, 'train/grad_norm': 1.050764560699463, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 13:11:08] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:11:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:13] train_sft.py:596 - step 1043: {'train/loss': 0.05773329734802246, 'train/grad_norm': 0.7904686331748962, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 13:11:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:11:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:18] train_sft.py:596 - step 1044: {'train/loss': 0.08878117799758911, 'train/grad_norm': 0.8050737380981445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 13:11:18] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:11:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:22] train_sft.py:596 - step 1045: {'train/loss': 0.09073549509048462, 'train/grad_norm': 1.1156259775161743, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:11:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:11:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:27] train_sft.py:596 - step 1046: {'train/loss': 0.05872011184692383, 'train/grad_norm': 0.733414351940155, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:11:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:11:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:32] train_sft.py:596 - step 1047: {'train/loss': 0.07149505615234375, 'train/grad_norm': 0.6413747072219849, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:11:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:11:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:36] train_sft.py:596 - step 1048: {'train/loss': 0.05593785643577576, 'train/grad_norm': 0.6258493065834045, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:11:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:11:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:41] train_sft.py:596 - step 1049: {'train/loss': 0.06897163391113281, 'train/grad_norm': 0.9255269169807434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:11:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:11:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:46] train_sft.py:596 - step 1050: {'train/loss': 0.07228529453277588, 'train/grad_norm': 1.049153447151184, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:11:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:11:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:51] train_sft.py:596 - step 1051: {'train/loss': 0.05382704734802246, 'train/grad_norm': 0.7818945646286011, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:11:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:11:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:11:55] train_sft.py:596 - step 1052: {'train/loss': 0.07109808921813965, 'train/grad_norm': 0.7238659262657166, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:11:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:11:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:00] train_sft.py:596 - step 1053: {'train/loss': 0.0636286549270153, 'train/grad_norm': 0.794245719909668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 13:12:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:12:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:05] train_sft.py:596 - step 1054: {'train/loss': 0.09033581614494324, 'train/grad_norm': 0.9722755551338196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 13:12:05] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:12:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:10] train_sft.py:596 - step 1055: {'train/loss': 0.06748509407043457, 'train/grad_norm': 0.8697340488433838, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 13:12:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:12:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:15] train_sft.py:596 - step 1056: {'train/loss': 0.05075627565383911, 'train/grad_norm': 1.0052233934402466, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:12:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:12:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:20] train_sft.py:596 - step 1057: {'train/loss': 0.06714391708374023, 'train/grad_norm': 0.6837692856788635, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:12:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:24] train_sft.py:596 - step 1058: {'train/loss': 0.06577587127685547, 'train/grad_norm': 0.6670582890510559, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:12:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:29] train_sft.py:596 - step 1059: {'train/loss': 0.06425905227661133, 'train/grad_norm': 0.8338897228240967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:12:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:34] train_sft.py:596 - step 1060: {'train/loss': 0.06516814231872559, 'train/grad_norm': 0.8384151458740234, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:12:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:38] train_sft.py:596 - step 1061: {'train/loss': 0.06881332397460938, 'train/grad_norm': 0.8864928483963013, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:12:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:43] train_sft.py:596 - step 1062: {'train/loss': 0.06534481048583984, 'train/grad_norm': 0.9840585589408875, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:12:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:48] train_sft.py:596 - step 1063: {'train/loss': 0.08391952514648438, 'train/grad_norm': 0.8623435497283936, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:12:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:12:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:52] train_sft.py:596 - step 1064: {'train/loss': 0.06634521484375, 'train/grad_norm': 0.7748553156852722, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:12:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:12:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:12:57] train_sft.py:596 - step 1065: {'train/loss': 0.07741451263427734, 'train/grad_norm': 0.7587529420852661, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:12:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:12:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:02] train_sft.py:596 - step 1066: {'train/loss': 0.05876731872558594, 'train/grad_norm': 0.6385567784309387, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 13:13:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:13:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:07] train_sft.py:596 - step 1067: {'train/loss': 0.06355810165405273, 'train/grad_norm': 0.7080130577087402, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:13:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:13:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:11] train_sft.py:596 - step 1068: {'train/loss': 0.05759000778198242, 'train/grad_norm': 0.799268364906311, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:13:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:13:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:16] train_sft.py:596 - step 1069: {'train/loss': 0.06487131118774414, 'train/grad_norm': 0.7179481983184814, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 13:13:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:13:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:21] train_sft.py:596 - step 1070: {'train/loss': 0.07031917572021484, 'train/grad_norm': 0.8117155432701111, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:13:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:13:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:26] train_sft.py:596 - step 1071: {'train/loss': 0.06938260793685913, 'train/grad_norm': 0.6930922865867615, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 13:13:26] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:13:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:30] train_sft.py:596 - step 1072: {'train/loss': 0.05879783630371094, 'train/grad_norm': 0.5725976824760437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:13:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:13:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:35] train_sft.py:596 - step 1073: {'train/loss': 0.059307098388671875, 'train/grad_norm': 0.8192502856254578, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:13:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:13:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:40] train_sft.py:596 - step 1074: {'train/loss': 0.05475473403930664, 'train/grad_norm': 0.5401118397712708, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:13:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:13:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:44] train_sft.py:596 - step 1075: {'train/loss': 0.06134235858917236, 'train/grad_norm': 0.7154665589332581, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:13:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:13:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:49] train_sft.py:596 - step 1076: {'train/loss': 0.05399894714355469, 'train/grad_norm': 0.6214245557785034, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:13:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:13:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:54] train_sft.py:596 - step 1077: {'train/loss': 0.07881581783294678, 'train/grad_norm': 0.8460092544555664, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:13:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:13:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:13:59] train_sft.py:596 - step 1078: {'train/loss': 0.05427074432373047, 'train/grad_norm': 0.6453240513801575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:13:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:13:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:03] train_sft.py:596 - step 1079: {'train/loss': 0.049895644187927246, 'train/grad_norm': 0.7695152759552002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:14:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:08] train_sft.py:596 - step 1080: {'train/loss': 0.05246257781982422, 'train/grad_norm': 0.6266458034515381, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:14:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:14:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:13] train_sft.py:596 - step 1081: {'train/loss': 0.07088208198547363, 'train/grad_norm': 0.6496828198432922, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:14:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:14:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:17] train_sft.py:596 - step 1082: {'train/loss': 0.07545089721679688, 'train/grad_norm': 0.7248531579971313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:14:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:22] train_sft.py:596 - step 1083: {'train/loss': 0.07076549530029297, 'train/grad_norm': 0.9302920699119568, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:14:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:27] train_sft.py:596 - step 1084: {'train/loss': 0.06599855422973633, 'train/grad_norm': 0.8742898106575012, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:14:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:31] train_sft.py:596 - step 1085: {'train/loss': 0.06654548645019531, 'train/grad_norm': 0.9976780414581299, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:14:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:36] train_sft.py:596 - step 1086: {'train/loss': 0.07822287082672119, 'train/grad_norm': 0.8455284833908081, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:14:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:41] train_sft.py:596 - step 1087: {'train/loss': 0.054406046867370605, 'train/grad_norm': 1.0998144149780273, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:14:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:14:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:45] train_sft.py:596 - step 1088: {'train/loss': 0.06316912174224854, 'train/grad_norm': 0.5659109950065613, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:14:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:50] train_sft.py:596 - step 1089: {'train/loss': 0.061202049255371094, 'train/grad_norm': 0.9450750946998596, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:14:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:55] train_sft.py:596 - step 1090: {'train/loss': 0.0749654769897461, 'train/grad_norm': 0.8047319650650024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:14:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:14:59] train_sft.py:596 - step 1091: {'train/loss': 0.0832681655883789, 'train/grad_norm': 0.9427050948143005, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:14:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:14:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:04] train_sft.py:596 - step 1092: {'train/loss': 0.07269430160522461, 'train/grad_norm': 1.1874868869781494, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:15:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:09] train_sft.py:596 - step 1093: {'train/loss': 0.0656280517578125, 'train/grad_norm': 0.9572976231575012, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:15:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:13] train_sft.py:596 - step 1094: {'train/loss': 0.07138684391975403, 'train/grad_norm': 0.5548728704452515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:15:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:18] train_sft.py:596 - step 1095: {'train/loss': 0.06740760803222656, 'train/grad_norm': 0.7058886289596558, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:15:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:23] train_sft.py:596 - step 1096: {'train/loss': 0.0677480697631836, 'train/grad_norm': 0.7499282956123352, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:15:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:15:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:28] train_sft.py:596 - step 1097: {'train/loss': 0.07059049606323242, 'train/grad_norm': 0.7756369709968567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:15:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:32] train_sft.py:596 - step 1098: {'train/loss': 0.0499577522277832, 'train/grad_norm': 0.8200909495353699, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:15:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:37] train_sft.py:596 - step 1099: {'train/loss': 0.06440877914428711, 'train/grad_norm': 0.7344719767570496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:15:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:41] train_sft.py:596 - step 1100: {'train/loss': 0.06458282470703125, 'train/grad_norm': 0.5689102411270142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:15:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:15:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:46] train_sft.py:596 - step 1101: {'train/loss': 0.050937652587890625, 'train/grad_norm': 0.8188652992248535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:15:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:51] train_sft.py:596 - step 1102: {'train/loss': 0.05905735492706299, 'train/grad_norm': 0.5010195970535278, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:15:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:15:55] train_sft.py:596 - step 1103: {'train/loss': 0.0709376335144043, 'train/grad_norm': 0.8852112293243408, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:15:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:15:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:00] train_sft.py:596 - step 1104: {'train/loss': 0.06806564331054688, 'train/grad_norm': 0.8051385283470154, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:16:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:16:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:05] train_sft.py:596 - step 1105: {'train/loss': 0.07210731506347656, 'train/grad_norm': 0.6396046876907349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:16:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:16:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:10] train_sft.py:596 - step 1106: {'train/loss': 0.074188232421875, 'train/grad_norm': 0.7421611547470093, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:16:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:16:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:14] train_sft.py:596 - step 1107: {'train/loss': 0.059932708740234375, 'train/grad_norm': 0.641139805316925, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:16:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:16:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:19] train_sft.py:596 - step 1108: {'train/loss': 0.06270313262939453, 'train/grad_norm': 0.80962735414505, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:16:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:16:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:24] train_sft.py:596 - step 1109: {'train/loss': 0.07104969024658203, 'train/grad_norm': 0.6609233617782593, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:16:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:16:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:29] train_sft.py:596 - step 1110: {'train/loss': 0.054660797119140625, 'train/grad_norm': 0.6795530915260315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:16:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:16:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:33] train_sft.py:596 - step 1111: {'train/loss': 0.045580387115478516, 'train/grad_norm': 0.9532174468040466, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:16:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:16:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:38] train_sft.py:596 - step 1112: {'train/loss': 0.0595165491104126, 'train/grad_norm': 0.8358287811279297, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:16:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:16:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:43] train_sft.py:596 - step 1113: {'train/loss': 0.07334232330322266, 'train/grad_norm': 0.7328811883926392, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 13:16:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:16:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:48] train_sft.py:596 - step 1114: {'train/loss': 0.05842113494873047, 'train/grad_norm': 0.6858499050140381, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:16:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:16:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:53] train_sft.py:596 - step 1115: {'train/loss': 0.06185483932495117, 'train/grad_norm': 0.7964680790901184, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:16:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:16:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:16:57] train_sft.py:596 - step 1116: {'train/loss': 0.05018901824951172, 'train/grad_norm': 0.617777407169342, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:16:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:16:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:02] train_sft.py:596 - step 1117: {'train/loss': 0.05554008483886719, 'train/grad_norm': 0.6939780712127686, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 13:17:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:17:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:07] train_sft.py:596 - step 1118: {'train/loss': 0.053084373474121094, 'train/grad_norm': 0.6961050629615784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:17:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:11] train_sft.py:596 - step 1119: {'train/loss': 0.05199170112609863, 'train/grad_norm': 0.511091947555542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:17:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:16] train_sft.py:596 - step 1120: {'train/loss': 0.05326080322265625, 'train/grad_norm': 0.6868767738342285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 13:17:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:17:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:21] train_sft.py:596 - step 1121: {'train/loss': 0.0686492919921875, 'train/grad_norm': 0.9941260814666748, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:17:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:25] train_sft.py:596 - step 1122: {'train/loss': 0.0850672721862793, 'train/grad_norm': 0.9340873956680298, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:17:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:30] train_sft.py:596 - step 1123: {'train/loss': 0.06540822982788086, 'train/grad_norm': 0.741180956363678, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:17:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:35] train_sft.py:596 - step 1124: {'train/loss': 0.05913352966308594, 'train/grad_norm': 0.9964238405227661, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:17:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:40] train_sft.py:596 - step 1125: {'train/loss': 0.0631704330444336, 'train/grad_norm': 0.6440449953079224, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:17:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:17:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:44] train_sft.py:596 - step 1126: {'train/loss': 0.052204787731170654, 'train/grad_norm': 0.7081976532936096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:17:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:49] train_sft.py:596 - step 1127: {'train/loss': 0.02686142921447754, 'train/grad_norm': 0.4705178439617157, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:17:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:17:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:54] train_sft.py:596 - step 1128: {'train/loss': 0.04023122787475586, 'train/grad_norm': 1.0259462594985962, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:17:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:17:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:17:59] train_sft.py:596 - step 1129: {'train/loss': 0.03819692134857178, 'train/grad_norm': 0.653998076915741, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:17:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:17:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:03] train_sft.py:596 - step 1130: {'train/loss': 0.03137668967247009, 'train/grad_norm': 0.610407292842865, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:18:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:18:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:08] train_sft.py:596 - step 1131: {'train/loss': 0.04080682992935181, 'train/grad_norm': 0.6139234304428101, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:18:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:18:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:12] train_sft.py:596 - step 1132: {'train/loss': 0.03260098397731781, 'train/grad_norm': 0.4935515522956848, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:18:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:18:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:17] train_sft.py:596 - step 1133: {'train/loss': 0.04291662573814392, 'train/grad_norm': 0.4016175866127014, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:18:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:18:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:22] train_sft.py:596 - step 1134: {'train/loss': 0.047614455223083496, 'train/grad_norm': 0.6508690118789673, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:18:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:18:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:27] train_sft.py:596 - step 1135: {'train/loss': 0.038542747497558594, 'train/grad_norm': 0.6859133839607239, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:18:27] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:18:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:32] train_sft.py:596 - step 1136: {'train/loss': 0.045972466468811035, 'train/grad_norm': 0.6097432971000671, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:18:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:18:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:36] train_sft.py:596 - step 1137: {'train/loss': 0.03484833240509033, 'train/grad_norm': 0.580539345741272, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:18:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:18:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:41] train_sft.py:596 - step 1138: {'train/loss': 0.045105695724487305, 'train/grad_norm': 0.8388985991477966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 13:18:41] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:18:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:46] train_sft.py:596 - step 1139: {'train/loss': 0.04578900337219238, 'train/grad_norm': 1.0736643075942993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:18:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:18:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:51] train_sft.py:596 - step 1140: {'train/loss': 0.036943674087524414, 'train/grad_norm': 0.584762454032898, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:18:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:18:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:18:56] train_sft.py:596 - step 1141: {'train/loss': 0.04547455906867981, 'train/grad_norm': 0.694017231464386, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:18:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:18:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:01] train_sft.py:596 - step 1142: {'train/loss': 0.04811692237854004, 'train/grad_norm': 0.9127660989761353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:19:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:05] train_sft.py:596 - step 1143: {'train/loss': 0.033743441104888916, 'train/grad_norm': 0.9139202237129211, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:19:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:10] train_sft.py:596 - step 1144: {'train/loss': 0.03488853573799133, 'train/grad_norm': 0.6684471368789673, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 13:19:10] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:19:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:15] train_sft.py:596 - step 1145: {'train/loss': 0.054067909717559814, 'train/grad_norm': 1.278257131576538, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:19:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:20] train_sft.py:596 - step 1146: {'train/loss': 0.03595827519893646, 'train/grad_norm': 0.6349871158599854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 13:19:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:19:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:25] train_sft.py:596 - step 1147: {'train/loss': 0.04943840205669403, 'train/grad_norm': 0.9428977966308594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:19:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:19:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:30] train_sft.py:596 - step 1148: {'train/loss': 0.038437485694885254, 'train/grad_norm': 0.8298284411430359, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 13:19:30] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:19:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:35] train_sft.py:596 - step 1149: {'train/loss': 0.027747273445129395, 'train/grad_norm': 0.4461555778980255, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 13:19:35] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:19:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:40] train_sft.py:596 - step 1150: {'train/loss': 0.04506504535675049, 'train/grad_norm': 0.5539063215255737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:19:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:19:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:45] train_sft.py:596 - step 1151: {'train/loss': 0.04369771480560303, 'train/grad_norm': 0.6643860340118408, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:19:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:49] train_sft.py:596 - step 1152: {'train/loss': 0.03219059109687805, 'train/grad_norm': 0.5914868116378784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:19:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:54] train_sft.py:596 - step 1153: {'train/loss': 0.03515613079071045, 'train/grad_norm': 0.534106969833374, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:19:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:19:59] train_sft.py:596 - step 1154: {'train/loss': 0.043105125427246094, 'train/grad_norm': 0.8333528637886047, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:19:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:19:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:04] train_sft.py:596 - step 1155: {'train/loss': 0.04173073172569275, 'train/grad_norm': 0.5400568842887878, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 13:20:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:20:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:09] train_sft.py:596 - step 1156: {'train/loss': 0.037160202860832214, 'train/grad_norm': 1.034956455230713, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:20:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:20:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:13] train_sft.py:596 - step 1157: {'train/loss': 0.03940993547439575, 'train/grad_norm': 0.5787507891654968, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:20:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:20:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:18] train_sft.py:596 - step 1158: {'train/loss': 0.043680429458618164, 'train/grad_norm': 0.929983913898468, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:20:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:20:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:23] train_sft.py:596 - step 1159: {'train/loss': 0.03594052791595459, 'train/grad_norm': 0.4965570867061615, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 13:20:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:20:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:28] train_sft.py:596 - step 1160: {'train/loss': 0.04069328308105469, 'train/grad_norm': 0.8544436097145081, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
[2025-12-29 13:20:28] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:20:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:33] train_sft.py:596 - step 1161: {'train/loss': 0.046037912368774414, 'train/grad_norm': 0.8075405955314636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:20:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:20:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:37] train_sft.py:596 - step 1162: {'train/loss': 0.04406082630157471, 'train/grad_norm': 0.8053080439567566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:20:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:20:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:42] train_sft.py:596 - step 1163: {'train/loss': 0.04894816875457764, 'train/grad_norm': 0.8395206332206726, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:20:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:20:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:47] train_sft.py:596 - step 1164: {'train/loss': 0.04057669639587402, 'train/grad_norm': 0.9946244955062866, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:20:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:20:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:52] train_sft.py:596 - step 1165: {'train/loss': 0.04064968228340149, 'train/grad_norm': 0.8327305912971497, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 13:20:52] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:20:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:20:57] train_sft.py:596 - step 1166: {'train/loss': 0.04072082042694092, 'train/grad_norm': 0.5098461508750916, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 13:20:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:20:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:02] train_sft.py:596 - step 1167: {'train/loss': 0.0517733097076416, 'train/grad_norm': 0.6574764847755432, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:21:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:21:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:07] train_sft.py:596 - step 1168: {'train/loss': 0.04555743932723999, 'train/grad_norm': 1.1176692247390747, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:21:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:21:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:12] train_sft.py:596 - step 1169: {'train/loss': 0.0426669716835022, 'train/grad_norm': 0.6527359485626221, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:21:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:21:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:16] train_sft.py:596 - step 1170: {'train/loss': 0.04517413675785065, 'train/grad_norm': 0.9767774939537048, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:21:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:21:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:21] train_sft.py:596 - step 1171: {'train/loss': 0.047423601150512695, 'train/grad_norm': 0.6141535043716431, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:21:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:21:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:27] train_sft.py:596 - step 1172: {'train/loss': 0.03662079572677612, 'train/grad_norm': 0.9442071914672852, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 13:21:27] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:21:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:31] train_sft.py:596 - step 1173: {'train/loss': 0.040976881980895996, 'train/grad_norm': 0.7199125289916992, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:21:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:21:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:37] train_sft.py:596 - step 1174: {'train/loss': 0.045135498046875, 'train/grad_norm': 0.8068061470985413, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.34s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.34s/it]
[2025-12-29 13:21:37] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 13:21:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:42] train_sft.py:596 - step 1175: {'train/loss': 0.04293090105056763, 'train/grad_norm': 0.9526795744895935, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:21:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:21:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:47] train_sft.py:596 - step 1176: {'train/loss': 0.03372502326965332, 'train/grad_norm': 0.66129469871521, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:21:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:21:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:51] train_sft.py:596 - step 1177: {'train/loss': 0.03983253240585327, 'train/grad_norm': 0.760921061038971, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:21:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:21:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:21:56] train_sft.py:596 - step 1178: {'train/loss': 0.036182403564453125, 'train/grad_norm': 0.7832937836647034, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:21:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:21:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:01] train_sft.py:596 - step 1179: {'train/loss': 0.04543173313140869, 'train/grad_norm': 0.6666610240936279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 13:22:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:22:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:06] train_sft.py:596 - step 1180: {'train/loss': 0.040961265563964844, 'train/grad_norm': 1.009962797164917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:22:06] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:22:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:11] train_sft.py:596 - step 1181: {'train/loss': 0.04293656349182129, 'train/grad_norm': 0.6585827469825745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 13:22:11] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:22:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:16] train_sft.py:596 - step 1182: {'train/loss': 0.05180975794792175, 'train/grad_norm': 1.0850176811218262, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
[2025-12-29 13:22:16] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:22:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:21] train_sft.py:596 - step 1183: {'train/loss': 0.05971163511276245, 'train/grad_norm': 0.9203302264213562, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:22:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:22:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:26] train_sft.py:596 - step 1184: {'train/loss': 0.039058148860931396, 'train/grad_norm': 0.5822980999946594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:22:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:22:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:31] train_sft.py:596 - step 1185: {'train/loss': 0.040862858295440674, 'train/grad_norm': 0.5075051784515381, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:22:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:22:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:36] train_sft.py:596 - step 1186: {'train/loss': 0.04548442363739014, 'train/grad_norm': 0.5828935503959656, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 13:22:36] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:22:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:41] train_sft.py:596 - step 1187: {'train/loss': 0.038114070892333984, 'train/grad_norm': 0.5192543268203735, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:22:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:22:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:45] train_sft.py:596 - step 1188: {'train/loss': 0.03898823261260986, 'train/grad_norm': 0.45987576246261597, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:22:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:22:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:50] train_sft.py:596 - step 1189: {'train/loss': 0.04461485147476196, 'train/grad_norm': 0.6752845644950867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:22:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:22:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:22:55] train_sft.py:596 - step 1190: {'train/loss': 0.06259727478027344, 'train/grad_norm': 0.6493651270866394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 13:22:55] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:22:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:01] train_sft.py:596 - step 1191: {'train/loss': 0.04144170880317688, 'train/grad_norm': 0.4983147382736206, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
[2025-12-29 13:23:01] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 13:23:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:06] train_sft.py:596 - step 1192: {'train/loss': 0.04498112201690674, 'train/grad_norm': 1.0640655755996704, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]
[2025-12-29 13:23:06] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 13:23:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:11] train_sft.py:596 - step 1193: {'train/loss': 0.029702425003051758, 'train/grad_norm': 0.5953245162963867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
[2025-12-29 13:23:11] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:23:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:16] train_sft.py:596 - step 1194: {'train/loss': 0.0519251823425293, 'train/grad_norm': 0.6287445425987244, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:23:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:23:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:22] train_sft.py:596 - step 1195: {'train/loss': 0.048013508319854736, 'train/grad_norm': 0.625021755695343, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 13:23:22] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:23:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:26] train_sft.py:596 - step 1196: {'train/loss': 0.03360128402709961, 'train/grad_norm': 0.7417227625846863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:23:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:23:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:31] train_sft.py:596 - step 1197: {'train/loss': 0.04854568839073181, 'train/grad_norm': 0.6058884263038635, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:23:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:23:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:37] train_sft.py:596 - step 1198: {'train/loss': 0.0339505672454834, 'train/grad_norm': 0.6960474252700806, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 13:23:37] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:23:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:41] train_sft.py:596 - step 1199: {'train/loss': 0.05189990997314453, 'train/grad_norm': 0.6450290083885193, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:23:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:23:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:46] train_sft.py:596 - step 1200: {'train/loss': 0.04588392376899719, 'train/grad_norm': 0.6506468653678894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:23:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:23:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:51] train_sft.py:596 - step 1201: {'train/loss': 0.048961639404296875, 'train/grad_norm': 0.778387188911438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:23:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:23:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:23:55] train_sft.py:596 - step 1202: {'train/loss': 0.047353923320770264, 'train/grad_norm': 0.9922879934310913, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:23:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:23:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:00] train_sft.py:596 - step 1203: {'train/loss': 0.03664243221282959, 'train/grad_norm': 0.5253586173057556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 13:24:00] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:24:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:05] train_sft.py:596 - step 1204: {'train/loss': 0.03532569855451584, 'train/grad_norm': 0.49032726883888245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:24:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:24:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:10] train_sft.py:596 - step 1205: {'train/loss': 0.045751988887786865, 'train/grad_norm': 0.8598242402076721, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 13:24:10] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:24:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:15] train_sft.py:596 - step 1206: {'train/loss': 0.04678606986999512, 'train/grad_norm': 0.8944405317306519, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 13:24:15] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:24:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:20] train_sft.py:596 - step 1207: {'train/loss': 0.04164896905422211, 'train/grad_norm': 0.5501585006713867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:24:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:24:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:25] train_sft.py:596 - step 1208: {'train/loss': 0.04955004155635834, 'train/grad_norm': 0.858465850353241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 13:24:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:24:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:30] train_sft.py:596 - step 1209: {'train/loss': 0.039509475231170654, 'train/grad_norm': 0.6270461678504944, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:24:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:24:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:34] train_sft.py:596 - step 1210: {'train/loss': 0.040665626525878906, 'train/grad_norm': 0.5690939426422119, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 13:24:34] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:24:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:39] train_sft.py:596 - step 1211: {'train/loss': 0.04742443561553955, 'train/grad_norm': 0.6581657528877258, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:24:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:24:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:44] train_sft.py:596 - step 1212: {'train/loss': 0.03304380178451538, 'train/grad_norm': 0.6427389979362488, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:24:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:24:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:49] train_sft.py:596 - step 1213: {'train/loss': 0.03886067867279053, 'train/grad_norm': 0.6301121711730957, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:24:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:24:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:54] train_sft.py:596 - step 1214: {'train/loss': 0.043294310569763184, 'train/grad_norm': 0.5377362966537476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:24:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:24:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:24:58] train_sft.py:596 - step 1215: {'train/loss': 0.05476713180541992, 'train/grad_norm': 2.489903211593628, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:24:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:24:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:03] train_sft.py:596 - step 1216: {'train/loss': 0.05322587490081787, 'train/grad_norm': 1.0085797309875488, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 13:25:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:25:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:08] train_sft.py:596 - step 1217: {'train/loss': 0.03242051601409912, 'train/grad_norm': 0.5254760384559631, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:25:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:25:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:13] train_sft.py:596 - step 1218: {'train/loss': 0.04593801498413086, 'train/grad_norm': 0.9068564176559448, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:25:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:25:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:18] train_sft.py:596 - step 1219: {'train/loss': 0.03420287370681763, 'train/grad_norm': 0.59754478931427, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:25:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:25:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:23] train_sft.py:596 - step 1220: {'train/loss': 0.038951873779296875, 'train/grad_norm': 0.9725098013877869, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]
[2025-12-29 13:25:23] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:25:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:28] train_sft.py:596 - step 1221: {'train/loss': 0.041391849517822266, 'train/grad_norm': 0.5906128287315369, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:25:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:25:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:33] train_sft.py:596 - step 1222: {'train/loss': 0.048182129859924316, 'train/grad_norm': 0.88347989320755, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 13:25:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:25:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:37] train_sft.py:596 - step 1223: {'train/loss': 0.03726375102996826, 'train/grad_norm': 0.7331905961036682, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:25:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:25:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:42] train_sft.py:596 - step 1224: {'train/loss': 0.03534579277038574, 'train/grad_norm': 0.6559865474700928, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:25:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:25:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:47] train_sft.py:596 - step 1225: {'train/loss': 0.04013484716415405, 'train/grad_norm': 0.604271650314331, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:25:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:25:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:52] train_sft.py:596 - step 1226: {'train/loss': 0.042086780071258545, 'train/grad_norm': 0.7457451820373535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:25:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:25:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:25:57] train_sft.py:596 - step 1227: {'train/loss': 0.057326316833496094, 'train/grad_norm': 1.1103057861328125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:25:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:25:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:02] train_sft.py:596 - step 1228: {'train/loss': 0.04494690150022507, 'train/grad_norm': 0.6093928217887878, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 13:26:02] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:26:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:06] train_sft.py:596 - step 1229: {'train/loss': 0.045351624488830566, 'train/grad_norm': 0.765192449092865, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:26:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:26:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:11] train_sft.py:596 - step 1230: {'train/loss': 0.04193568229675293, 'train/grad_norm': 0.5706819891929626, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:26:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:26:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:16] train_sft.py:596 - step 1231: {'train/loss': 0.05564570426940918, 'train/grad_norm': 0.8226516246795654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:26:16] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:26:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:21] train_sft.py:596 - step 1232: {'train/loss': 0.04876828193664551, 'train/grad_norm': 0.5821918845176697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 13:26:21] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:26:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:26] train_sft.py:596 - step 1233: {'train/loss': 0.04274451732635498, 'train/grad_norm': 0.673923671245575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:26:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:26:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:30] train_sft.py:596 - step 1234: {'train/loss': 0.04353833198547363, 'train/grad_norm': 0.5129115581512451, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:26:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:26:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:35] train_sft.py:596 - step 1235: {'train/loss': 0.04479575157165527, 'train/grad_norm': 0.817175030708313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:26:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:26:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:40] train_sft.py:596 - step 1236: {'train/loss': 0.04521465301513672, 'train/grad_norm': 0.5837674736976624, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:26:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:26:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:44] train_sft.py:596 - step 1237: {'train/loss': 0.04762840270996094, 'train/grad_norm': 0.6511678695678711, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:26:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:26:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:49] train_sft.py:596 - step 1238: {'train/loss': 0.04426693916320801, 'train/grad_norm': 0.6738446950912476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:26:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:26:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:53] train_sft.py:596 - step 1239: {'train/loss': 0.03915297985076904, 'train/grad_norm': 0.6515346169471741, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:26:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:26:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:26:58] train_sft.py:596 - step 1240: {'train/loss': 0.03716397285461426, 'train/grad_norm': 0.6208384037017822, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:26:58] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:26:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:03] train_sft.py:596 - step 1241: {'train/loss': 0.05320143699645996, 'train/grad_norm': 0.5472801327705383, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:27:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:27:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:08] train_sft.py:596 - step 1242: {'train/loss': 0.04406314343214035, 'train/grad_norm': 1.9424748420715332, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:27:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:27:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:12] train_sft.py:596 - step 1243: {'train/loss': 0.04912722110748291, 'train/grad_norm': 0.8085524439811707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:27:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:27:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:17] train_sft.py:596 - step 1244: {'train/loss': 0.040065646171569824, 'train/grad_norm': 0.5383422374725342, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 13:27:17] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:27:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:22] train_sft.py:596 - step 1245: {'train/loss': 0.04596686363220215, 'train/grad_norm': 0.6516022086143494, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:27:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:27] train_sft.py:596 - step 1246: {'train/loss': 0.04176132380962372, 'train/grad_norm': 0.46500280499458313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:27:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:27:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:31] train_sft.py:596 - step 1247: {'train/loss': 0.04266810417175293, 'train/grad_norm': 0.6868709325790405, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:27:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:36] train_sft.py:596 - step 1248: {'train/loss': 0.06097424030303955, 'train/grad_norm': 0.9252196550369263, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:27:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:41] train_sft.py:596 - step 1249: {'train/loss': 0.0554114431142807, 'train/grad_norm': 0.9700833559036255, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:27:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:45] train_sft.py:596 - step 1250: {'train/loss': 0.05031871795654297, 'train/grad_norm': 0.6051223874092102, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:27:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:50] train_sft.py:596 - step 1251: {'train/loss': 0.05586051940917969, 'train/grad_norm': 0.735957145690918, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:27:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:55] train_sft.py:596 - step 1252: {'train/loss': 0.042702674865722656, 'train/grad_norm': 0.6640881299972534, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:27:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:27:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:27:59] train_sft.py:596 - step 1253: {'train/loss': 0.0407024621963501, 'train/grad_norm': 0.49643638730049133, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:27:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:04] train_sft.py:596 - step 1254: {'train/loss': 0.04646730422973633, 'train/grad_norm': 0.6055163741111755, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:28:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:09] train_sft.py:596 - step 1255: {'train/loss': 0.05274486541748047, 'train/grad_norm': 0.8742076754570007, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:28:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:28:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:13] train_sft.py:596 - step 1256: {'train/loss': 0.042906761169433594, 'train/grad_norm': 0.8422278761863708, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 13:28:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:28:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:18] train_sft.py:596 - step 1257: {'train/loss': 0.04914450645446777, 'train/grad_norm': 0.7276358604431152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:28:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:23] train_sft.py:596 - step 1258: {'train/loss': 0.04351067543029785, 'train/grad_norm': 0.5045609474182129, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:28:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:27] train_sft.py:596 - step 1259: {'train/loss': 0.04740893840789795, 'train/grad_norm': 0.6641145348548889, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:28:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:32] train_sft.py:596 - step 1260: {'train/loss': 0.041846513748168945, 'train/grad_norm': 0.8333578705787659, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:28:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:37] train_sft.py:596 - step 1261: {'train/loss': 0.050963401794433594, 'train/grad_norm': 0.6676919460296631, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:28:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:28:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:42] train_sft.py:596 - step 1262: {'train/loss': 0.0398714542388916, 'train/grad_norm': 0.613240659236908, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:28:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:46] train_sft.py:596 - step 1263: {'train/loss': 0.04291725158691406, 'train/grad_norm': 0.5448620319366455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:28:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:51] train_sft.py:596 - step 1264: {'train/loss': 0.04246491193771362, 'train/grad_norm': 0.5620182156562805, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:28:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:28:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:28:56] train_sft.py:596 - step 1265: {'train/loss': 0.04315376281738281, 'train/grad_norm': 0.4702817499637604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:28:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:28:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:00] train_sft.py:596 - step 1266: {'train/loss': 0.041667938232421875, 'train/grad_norm': 0.8480595350265503, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:29:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:05] train_sft.py:596 - step 1267: {'train/loss': 0.05107146501541138, 'train/grad_norm': 0.5941917896270752, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:29:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:29:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:10] train_sft.py:596 - step 1268: {'train/loss': 0.04465079307556152, 'train/grad_norm': 0.6360633373260498, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:29:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:29:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:15] train_sft.py:596 - step 1269: {'train/loss': 0.058756351470947266, 'train/grad_norm': 0.6705027222633362, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:29:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:20] train_sft.py:596 - step 1270: {'train/loss': 0.035836219787597656, 'train/grad_norm': 0.4360555112361908, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:29:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:24] train_sft.py:596 - step 1271: {'train/loss': 0.044071197509765625, 'train/grad_norm': 0.8813905119895935, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:29:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:29] train_sft.py:596 - step 1272: {'train/loss': 0.03788411617279053, 'train/grad_norm': 0.5245540738105774, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:29:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:34] train_sft.py:596 - step 1273: {'train/loss': 0.04174286127090454, 'train/grad_norm': 0.6532356142997742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:29:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:38] train_sft.py:596 - step 1274: {'train/loss': 0.052240848541259766, 'train/grad_norm': 0.9799224138259888, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 13:29:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:29:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:43] train_sft.py:596 - step 1275: {'train/loss': 0.05169215798377991, 'train/grad_norm': 0.4845806360244751, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 13:29:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:29:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:48] train_sft.py:596 - step 1276: {'train/loss': 0.04241853952407837, 'train/grad_norm': 0.5044953227043152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:29:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:29:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:52] train_sft.py:596 - step 1277: {'train/loss': 0.043476104736328125, 'train/grad_norm': 0.8835415244102478, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 13:29:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:29:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:29:57] train_sft.py:596 - step 1278: {'train/loss': 0.056678712368011475, 'train/grad_norm': 0.681209921836853, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:29:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:29:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:02] train_sft.py:596 - step 1279: {'train/loss': 0.056447774171829224, 'train/grad_norm': 0.9746441841125488, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:30:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:30:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:06] train_sft.py:596 - step 1280: {'train/loss': 0.04835021495819092, 'train/grad_norm': 0.5996226668357849, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:30:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:11] train_sft.py:596 - step 1281: {'train/loss': 0.0420536994934082, 'train/grad_norm': 0.7728089094161987, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:30:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:16] train_sft.py:596 - step 1282: {'train/loss': 0.08771467208862305, 'train/grad_norm': 0.6152902841567993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:30:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:20] train_sft.py:596 - step 1283: {'train/loss': 0.061937808990478516, 'train/grad_norm': 0.9483927488327026, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:30:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:30:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:25] train_sft.py:596 - step 1284: {'train/loss': 0.05384540557861328, 'train/grad_norm': 0.6665802597999573, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:30:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:30:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:29] train_sft.py:596 - step 1285: {'train/loss': 0.039521217346191406, 'train/grad_norm': 0.7248374223709106, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:30:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:30:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:34] train_sft.py:596 - step 1286: {'train/loss': 0.04265463352203369, 'train/grad_norm': 0.9075355529785156, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:30:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:39] train_sft.py:596 - step 1287: {'train/loss': 0.053128331899642944, 'train/grad_norm': 0.5144885778427124, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 13:30:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:30:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:44] train_sft.py:596 - step 1288: {'train/loss': 0.05564916133880615, 'train/grad_norm': 0.8721761703491211, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:30:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:49] train_sft.py:596 - step 1289: {'train/loss': 0.04555916786193848, 'train/grad_norm': 0.5696735978126526, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 13:30:49] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:30:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:53] train_sft.py:596 - step 1290: {'train/loss': 0.03889390826225281, 'train/grad_norm': 0.4278826117515564, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:30:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:30:58] train_sft.py:596 - step 1291: {'train/loss': 0.04193711280822754, 'train/grad_norm': 0.5768011808395386, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:30:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:30:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:03] train_sft.py:596 - step 1292: {'train/loss': 0.044220924377441406, 'train/grad_norm': 0.4614652097225189, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:31:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:31:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:07] train_sft.py:596 - step 1293: {'train/loss': 0.03614211082458496, 'train/grad_norm': 0.7058893442153931, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 13:31:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:31:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:12] train_sft.py:596 - step 1294: {'train/loss': 0.03900003433227539, 'train/grad_norm': 0.6282778978347778, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:31:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:31:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:17] train_sft.py:596 - step 1295: {'train/loss': 0.04055720567703247, 'train/grad_norm': 0.5790139436721802, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:31:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:31:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:21] train_sft.py:596 - step 1296: {'train/loss': 0.044715702533721924, 'train/grad_norm': 0.7345587015151978, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:31:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:31:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:26] train_sft.py:596 - step 1297: {'train/loss': 0.05050995945930481, 'train/grad_norm': 0.6835594773292542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:31:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:31:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:31] train_sft.py:596 - step 1298: {'train/loss': 0.05225290358066559, 'train/grad_norm': 0.8942630290985107, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:31:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:31:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:35] train_sft.py:596 - step 1299: {'train/loss': 0.049797892570495605, 'train/grad_norm': 1.022586703300476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:31:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:31:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:40] train_sft.py:596 - step 1300: {'train/loss': 0.04383492469787598, 'train/grad_norm': 0.582607626914978, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:31:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:31:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:45] train_sft.py:596 - step 1301: {'train/loss': 0.04122328758239746, 'train/grad_norm': 0.6777315735816956, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:31:45] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:31:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:50] train_sft.py:596 - step 1302: {'train/loss': 0.05135941505432129, 'train/grad_norm': 0.8393262028694153, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 13:31:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:31:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:54] train_sft.py:596 - step 1303: {'train/loss': 0.0499497652053833, 'train/grad_norm': 0.9819392561912537, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:31:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:31:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:31:59] train_sft.py:596 - step 1304: {'train/loss': 0.047742247581481934, 'train/grad_norm': 0.7656704783439636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:31:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:31:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:04] train_sft.py:596 - step 1305: {'train/loss': 0.04871758818626404, 'train/grad_norm': 0.8008475303649902, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:32:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:32:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:08] train_sft.py:596 - step 1306: {'train/loss': 0.046251773834228516, 'train/grad_norm': 0.746295154094696, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:32:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:13] train_sft.py:596 - step 1307: {'train/loss': 0.04596257209777832, 'train/grad_norm': 0.8442562818527222, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:32:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:32:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:18] train_sft.py:596 - step 1308: {'train/loss': 0.04676485061645508, 'train/grad_norm': 0.644141435623169, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 13:32:18] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:32:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:23] train_sft.py:596 - step 1309: {'train/loss': 0.05995601415634155, 'train/grad_norm': 0.7029004693031311, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:32:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:27] train_sft.py:596 - step 1310: {'train/loss': 0.05520272254943848, 'train/grad_norm': 0.5724254250526428, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:32:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:32] train_sft.py:596 - step 1311: {'train/loss': 0.04554904252290726, 'train/grad_norm': 0.497942179441452, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 13:32:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:32:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:37] train_sft.py:596 - step 1312: {'train/loss': 0.04550957679748535, 'train/grad_norm': 0.5077918767929077, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:32:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:32:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:42] train_sft.py:596 - step 1313: {'train/loss': 0.053884267807006836, 'train/grad_norm': 0.5674067735671997, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:32:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:46] train_sft.py:596 - step 1314: {'train/loss': 0.044467926025390625, 'train/grad_norm': 0.6078858971595764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:32:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:51] train_sft.py:596 - step 1315: {'train/loss': 0.047014713287353516, 'train/grad_norm': 0.7630891799926758, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:32:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:32:56] train_sft.py:596 - step 1316: {'train/loss': 0.03286135196685791, 'train/grad_norm': 0.5866273045539856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:32:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:32:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:01] train_sft.py:596 - step 1317: {'train/loss': 0.04241979122161865, 'train/grad_norm': 0.670868992805481, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 13:33:01] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:33:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:06] train_sft.py:596 - step 1318: {'train/loss': 0.05094742774963379, 'train/grad_norm': 1.0168497562408447, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:33:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:33:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:10] train_sft.py:596 - step 1319: {'train/loss': 0.0387725830078125, 'train/grad_norm': 0.4699019491672516, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:33:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:33:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:15] train_sft.py:596 - step 1320: {'train/loss': 0.0478152334690094, 'train/grad_norm': 0.7345377206802368, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 13:33:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:33:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:20] train_sft.py:596 - step 1321: {'train/loss': 0.04396286606788635, 'train/grad_norm': 0.6335809230804443, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:33:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:33:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:25] train_sft.py:596 - step 1322: {'train/loss': 0.05428934097290039, 'train/grad_norm': 0.6917856335639954, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:33:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:33:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:30] train_sft.py:596 - step 1323: {'train/loss': 0.045577824115753174, 'train/grad_norm': 1.0057449340820312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:33:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:33:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:35] train_sft.py:596 - step 1324: {'train/loss': 0.04494762420654297, 'train/grad_norm': 0.8850182294845581, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:33:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:33:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:39] train_sft.py:596 - step 1325: {'train/loss': 0.05179035663604736, 'train/grad_norm': 0.9974520802497864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:33:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:33:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:44] train_sft.py:596 - step 1326: {'train/loss': 0.053039416670799255, 'train/grad_norm': 0.7397118210792542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 13:33:44] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:33:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:50] train_sft.py:596 - step 1327: {'train/loss': 0.045612648129463196, 'train/grad_norm': 0.9161901473999023, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 13:33:50] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:33:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:55] train_sft.py:596 - step 1328: {'train/loss': 0.043292999267578125, 'train/grad_norm': 0.5886970162391663, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:33:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:33:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:33:59] train_sft.py:596 - step 1329: {'train/loss': 0.05323600769042969, 'train/grad_norm': 0.7806301116943359, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 13:33:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:34:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:04] train_sft.py:596 - step 1330: {'train/loss': 0.054633140563964844, 'train/grad_norm': 0.8425989151000977, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:34:04] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:34:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:09] train_sft.py:596 - step 1331: {'train/loss': 0.03958374261856079, 'train/grad_norm': 0.5368055105209351, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 13:34:09] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:34:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:15] train_sft.py:596 - step 1332: {'train/loss': 0.03844165802001953, 'train/grad_norm': 0.7799439430236816, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 13:34:15] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:34:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:20] train_sft.py:596 - step 1333: {'train/loss': 0.043162524700164795, 'train/grad_norm': 0.5673813819885254, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 13:34:20] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:34:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:25] train_sft.py:596 - step 1334: {'train/loss': 0.06547999382019043, 'train/grad_norm': 1.2768079042434692, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:34:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:34:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:29] train_sft.py:596 - step 1335: {'train/loss': 0.04635262489318848, 'train/grad_norm': 0.664154052734375, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:34:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:34:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:34] train_sft.py:596 - step 1336: {'train/loss': 0.0593867301940918, 'train/grad_norm': 0.7438097596168518, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:34:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:34:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:39] train_sft.py:596 - step 1337: {'train/loss': 0.05543720722198486, 'train/grad_norm': 0.6943041086196899, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:34:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:34:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:44] train_sft.py:596 - step 1338: {'train/loss': 0.06665515899658203, 'train/grad_norm': 1.0702247619628906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:34:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:34:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:49] train_sft.py:596 - step 1339: {'train/loss': 0.04362034797668457, 'train/grad_norm': 2.4424867630004883, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:34:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:34:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:53] train_sft.py:596 - step 1340: {'train/loss': 0.04428386688232422, 'train/grad_norm': 0.5200169682502747, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:34:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:34:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:34:58] train_sft.py:596 - step 1341: {'train/loss': 0.050432682037353516, 'train/grad_norm': 0.8382330536842346, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 13:34:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:34:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:03] train_sft.py:596 - step 1342: {'train/loss': 0.04389595985412598, 'train/grad_norm': 0.6573459506034851, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 13:35:03] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:35:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:08] train_sft.py:596 - step 1343: {'train/loss': 0.04583597183227539, 'train/grad_norm': 0.9954423904418945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 13:35:08] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:35:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:13] train_sft.py:596 - step 1344: {'train/loss': 0.04728221893310547, 'train/grad_norm': 0.7997820973396301, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:35:13] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:35:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:18] train_sft.py:596 - step 1345: {'train/loss': 0.054729580879211426, 'train/grad_norm': 0.8873157501220703, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:35:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:35:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:23] train_sft.py:596 - step 1346: {'train/loss': 0.052689194679260254, 'train/grad_norm': 0.6454179286956787, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:35:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:35:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:27] train_sft.py:596 - step 1347: {'train/loss': 0.054503788240253925, 'train/grad_norm': 0.8153381943702698, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:35:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:35:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:32] train_sft.py:596 - step 1348: {'train/loss': 0.051276251673698425, 'train/grad_norm': 0.6713289022445679, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:35:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:35:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:37] train_sft.py:596 - step 1349: {'train/loss': 0.037149667739868164, 'train/grad_norm': 0.5690335631370544, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:35:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:35:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:42] train_sft.py:596 - step 1350: {'train/loss': 0.055893659591674805, 'train/grad_norm': 0.9283851385116577, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.10s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.10s/it]
[2025-12-29 13:35:42] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:35:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:47] train_sft.py:596 - step 1351: {'train/loss': 0.029647469520568848, 'train/grad_norm': 0.7069994807243347, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:35:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:35:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:52] train_sft.py:596 - step 1352: {'train/loss': 0.026523232460021973, 'train/grad_norm': 0.4311129152774811, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 13:35:52] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:35:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:35:57] train_sft.py:596 - step 1353: {'train/loss': 0.028266072273254395, 'train/grad_norm': 0.6056615710258484, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 13:35:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:35:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:02] train_sft.py:596 - step 1354: {'train/loss': 0.02546742558479309, 'train/grad_norm': 0.29642197489738464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 13:36:02] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:36:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:07] train_sft.py:596 - step 1355: {'train/loss': 0.030136145651340485, 'train/grad_norm': 0.7730493545532227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:36:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:36:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:11] train_sft.py:596 - step 1356: {'train/loss': 0.03162658214569092, 'train/grad_norm': 0.45372703671455383, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:36:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:36:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:16] train_sft.py:596 - step 1357: {'train/loss': 0.02782769501209259, 'train/grad_norm': 0.5913283824920654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:36:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:36:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:21] train_sft.py:596 - step 1358: {'train/loss': 0.03529129922389984, 'train/grad_norm': 1.1590555906295776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:36:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:36:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:25] train_sft.py:596 - step 1359: {'train/loss': 0.022493675351142883, 'train/grad_norm': 0.46241068840026855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:36:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:36:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:30] train_sft.py:596 - step 1360: {'train/loss': 0.031012356281280518, 'train/grad_norm': 0.7165011167526245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:36:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:36:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:35] train_sft.py:596 - step 1361: {'train/loss': 0.0356835201382637, 'train/grad_norm': 1.4744021892547607, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:36:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:36:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:40] train_sft.py:596 - step 1362: {'train/loss': 0.02257406711578369, 'train/grad_norm': 0.4321894943714142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 13:36:40] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:36:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:45] train_sft.py:596 - step 1363: {'train/loss': 0.03333929181098938, 'train/grad_norm': 0.8357711434364319, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:36:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:36:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:50] train_sft.py:596 - step 1364: {'train/loss': 0.03263479471206665, 'train/grad_norm': 0.6109048128128052, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 13:36:50] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:36:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:36:55] train_sft.py:596 - step 1365: {'train/loss': 0.025828301906585693, 'train/grad_norm': 0.43492791056632996, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:36:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:36:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:00] train_sft.py:596 - step 1366: {'train/loss': 0.02628327254205942, 'train/grad_norm': 0.8676248788833618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 13:37:00] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:37:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:05] train_sft.py:596 - step 1367: {'train/loss': 0.02520434558391571, 'train/grad_norm': 0.4717755913734436, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:37:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:37:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:10] train_sft.py:596 - step 1368: {'train/loss': 0.03318013250827789, 'train/grad_norm': 0.7486814856529236, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:37:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:37:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:15] train_sft.py:596 - step 1369: {'train/loss': 0.02318859100341797, 'train/grad_norm': 0.5866804122924805, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:37:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:37:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:20] train_sft.py:596 - step 1370: {'train/loss': 0.03200414776802063, 'train/grad_norm': 0.7426635026931763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 13:37:20] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:37:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:25] train_sft.py:596 - step 1371: {'train/loss': 0.0326346755027771, 'train/grad_norm': 0.742664635181427, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:37:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:37:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:30] train_sft.py:596 - step 1372: {'train/loss': 0.026083707809448242, 'train/grad_norm': 0.6150866746902466, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:37:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:37:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:35] train_sft.py:596 - step 1373: {'train/loss': 0.027037769556045532, 'train/grad_norm': 0.38812628388404846, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:37:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:37:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:40] train_sft.py:596 - step 1374: {'train/loss': 0.025933101773262024, 'train/grad_norm': 0.39796051383018494, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 13:37:40] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:37:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:45] train_sft.py:596 - step 1375: {'train/loss': 0.02620697021484375, 'train/grad_norm': 0.5803588628768921, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:37:45] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:37:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:50] train_sft.py:596 - step 1376: {'train/loss': 0.028620243072509766, 'train/grad_norm': 0.5716837048530579, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 13:37:50] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:37:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:37:55] train_sft.py:596 - step 1377: {'train/loss': 0.02785181999206543, 'train/grad_norm': 0.774677574634552, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:37:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:37:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:00] train_sft.py:596 - step 1378: {'train/loss': 0.02933603525161743, 'train/grad_norm': 0.847449004650116, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:38:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:38:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:05] train_sft.py:596 - step 1379: {'train/loss': 0.031102895736694336, 'train/grad_norm': 0.6521562933921814, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:38:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:38:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:09] train_sft.py:596 - step 1380: {'train/loss': 0.03086555004119873, 'train/grad_norm': 0.6046494245529175, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:38:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:38:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:14] train_sft.py:596 - step 1381: {'train/loss': 0.03149604797363281, 'train/grad_norm': 0.6696552038192749, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:38:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:38:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:19] train_sft.py:596 - step 1382: {'train/loss': 0.03850269317626953, 'train/grad_norm': 0.6056433320045471, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:38:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:38:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:24] train_sft.py:596 - step 1383: {'train/loss': 0.033016324043273926, 'train/grad_norm': 0.3521866500377655, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 13:38:24] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:38:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:29] train_sft.py:596 - step 1384: {'train/loss': 0.04101109504699707, 'train/grad_norm': 0.6888659000396729, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:38:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:38:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:34] train_sft.py:596 - step 1385: {'train/loss': 0.03425002098083496, 'train/grad_norm': 0.7651308178901672, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:38:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:38:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:38] train_sft.py:596 - step 1386: {'train/loss': 0.017483770847320557, 'train/grad_norm': 0.5382437109947205, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:38:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:38:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:43] train_sft.py:596 - step 1387: {'train/loss': 0.019033387303352356, 'train/grad_norm': 0.35005858540534973, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:38:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:38:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:48] train_sft.py:596 - step 1388: {'train/loss': 0.03695449233055115, 'train/grad_norm': 0.7246442437171936, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:38:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:38:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:52] train_sft.py:596 - step 1389: {'train/loss': 0.044481515884399414, 'train/grad_norm': 1.08547043800354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:38:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:38:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:38:57] train_sft.py:596 - step 1390: {'train/loss': 0.02648162841796875, 'train/grad_norm': 0.475003182888031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:38:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:38:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:02] train_sft.py:596 - step 1391: {'train/loss': 0.03594788536429405, 'train/grad_norm': 0.8439546823501587, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:39:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:39:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:07] train_sft.py:596 - step 1392: {'train/loss': 0.028067827224731445, 'train/grad_norm': 0.48678329586982727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:39:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:39:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:12] train_sft.py:596 - step 1393: {'train/loss': 0.03008633852005005, 'train/grad_norm': 0.7437944412231445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:39:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:39:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:17] train_sft.py:596 - step 1394: {'train/loss': 0.04559081792831421, 'train/grad_norm': 0.7055589556694031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 13:39:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:39:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:22] train_sft.py:596 - step 1395: {'train/loss': 0.024768874049186707, 'train/grad_norm': 0.5662913918495178, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:39:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:39:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:26] train_sft.py:596 - step 1396: {'train/loss': 0.027867138385772705, 'train/grad_norm': 0.8308945894241333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:39:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:39:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:31] train_sft.py:596 - step 1397: {'train/loss': 0.044507503509521484, 'train/grad_norm': 1.0125962495803833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:39:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:39:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:36] train_sft.py:596 - step 1398: {'train/loss': 0.02604222297668457, 'train/grad_norm': 0.6049816608428955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:39:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:39:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:41] train_sft.py:596 - step 1399: {'train/loss': 0.03395962715148926, 'train/grad_norm': 0.7140244841575623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:39:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:39:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:46] train_sft.py:596 - step 1400: {'train/loss': 0.03366708755493164, 'train/grad_norm': 0.8176833987236023, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 13:39:46] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:39:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:51] train_sft.py:596 - step 1401: {'train/loss': 0.03587442636489868, 'train/grad_norm': 0.5764686465263367, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:39:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:39:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:39:55] train_sft.py:596 - step 1402: {'train/loss': 0.024619996547698975, 'train/grad_norm': 0.6185972094535828, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:39:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:39:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:00] train_sft.py:596 - step 1403: {'train/loss': 0.02233743667602539, 'train/grad_norm': 0.443097323179245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:40:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:40:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:05] train_sft.py:596 - step 1404: {'train/loss': 0.033673644065856934, 'train/grad_norm': 0.5338577628135681, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 13:40:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:40:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:10] train_sft.py:596 - step 1405: {'train/loss': 0.031058669090270996, 'train/grad_norm': 0.48909953236579895, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 13:40:10] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:40:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:15] train_sft.py:596 - step 1406: {'train/loss': 0.03632688522338867, 'train/grad_norm': 0.6197925209999084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:40:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:40:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:20] train_sft.py:596 - step 1407: {'train/loss': 0.033336758613586426, 'train/grad_norm': 0.6071475744247437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 13:40:20] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:40:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:25] train_sft.py:596 - step 1408: {'train/loss': 0.030666708946228027, 'train/grad_norm': 0.5205809473991394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:40:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:40:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:30] train_sft.py:596 - step 1409: {'train/loss': 0.030234336853027344, 'train/grad_norm': 0.9584843516349792, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:40:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:40:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:35] train_sft.py:596 - step 1410: {'train/loss': 0.03061056137084961, 'train/grad_norm': 0.6593112349510193, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.05s/it]
[2025-12-29 13:40:35] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:40:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:40] train_sft.py:596 - step 1411: {'train/loss': 0.023592829704284668, 'train/grad_norm': 0.5979572534561157, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 13:40:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:40:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:45] train_sft.py:596 - step 1412: {'train/loss': 0.032082557678222656, 'train/grad_norm': 0.6481330990791321, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.23s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.23s/it]
[2025-12-29 13:40:45] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 13:40:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:50] train_sft.py:596 - step 1413: {'train/loss': 0.03994578868150711, 'train/grad_norm': 0.517657995223999, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:40:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:40:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:40:55] train_sft.py:596 - step 1414: {'train/loss': 0.03416064381599426, 'train/grad_norm': 0.5916547775268555, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:40:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:40:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:00] train_sft.py:596 - step 1415: {'train/loss': 0.025447309017181396, 'train/grad_norm': 0.7358474135398865, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 13:41:00] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:41:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:04] train_sft.py:596 - step 1416: {'train/loss': 0.06259916722774506, 'train/grad_norm': 0.6786448359489441, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:41:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:09] train_sft.py:596 - step 1417: {'train/loss': 0.029285669326782227, 'train/grad_norm': 0.5340920090675354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:41:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:41:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:14] train_sft.py:596 - step 1418: {'train/loss': 0.03547501564025879, 'train/grad_norm': 0.6173502206802368, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:41:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:19] train_sft.py:596 - step 1419: {'train/loss': 0.03767681121826172, 'train/grad_norm': 0.7207173705101013, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:41:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:23] train_sft.py:596 - step 1420: {'train/loss': 0.037209928035736084, 'train/grad_norm': 0.7934708595275879, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:41:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:28] train_sft.py:596 - step 1421: {'train/loss': 0.03076028823852539, 'train/grad_norm': 0.5683615803718567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:41:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:33] train_sft.py:596 - step 1422: {'train/loss': 0.04083928465843201, 'train/grad_norm': 0.9345414042472839, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:41:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:37] train_sft.py:596 - step 1423: {'train/loss': 0.03001987934112549, 'train/grad_norm': 0.5214449167251587, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:41:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:42] train_sft.py:596 - step 1424: {'train/loss': 0.03712084889411926, 'train/grad_norm': 0.7396018505096436, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 13:41:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:41:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:47] train_sft.py:596 - step 1425: {'train/loss': 0.03215700387954712, 'train/grad_norm': 0.7969262599945068, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:41:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:52] train_sft.py:596 - step 1426: {'train/loss': 0.029346853494644165, 'train/grad_norm': 0.5887365937232971, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:41:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:41:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:41:56] train_sft.py:596 - step 1427: {'train/loss': 0.04674851894378662, 'train/grad_norm': 1.1263360977172852, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:41:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:41:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:01] train_sft.py:596 - step 1428: {'train/loss': 0.030959725379943848, 'train/grad_norm': 0.5337680578231812, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:42:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:42:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:05] train_sft.py:596 - step 1429: {'train/loss': 0.038303375244140625, 'train/grad_norm': 0.9063283205032349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:42:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:42:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:10] train_sft.py:596 - step 1430: {'train/loss': 0.031279146671295166, 'train/grad_norm': 0.4666905701160431, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:42:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:42:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:15] train_sft.py:596 - step 1431: {'train/loss': 0.036879539489746094, 'train/grad_norm': 1.1176772117614746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:42:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:42:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:20] train_sft.py:596 - step 1432: {'train/loss': 0.02958807349205017, 'train/grad_norm': 0.7098857164382935, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 13:42:20] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:42:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:24] train_sft.py:596 - step 1433: {'train/loss': 0.042875826358795166, 'train/grad_norm': 0.643770158290863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:42:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:42:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:29] train_sft.py:596 - step 1434: {'train/loss': 0.02728414535522461, 'train/grad_norm': 0.40373867750167847, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:42:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:42:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:34] train_sft.py:596 - step 1435: {'train/loss': 0.042804837226867676, 'train/grad_norm': 0.7625721096992493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:42:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:42:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:38] train_sft.py:596 - step 1436: {'train/loss': 0.025044798851013184, 'train/grad_norm': 0.6192132830619812, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:42:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:42:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:43] train_sft.py:596 - step 1437: {'train/loss': 0.04193335771560669, 'train/grad_norm': 0.5645542144775391, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 13:42:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:42:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:48] train_sft.py:596 - step 1438: {'train/loss': 0.02809622883796692, 'train/grad_norm': 0.5368117690086365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:42:48] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:42:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:52] train_sft.py:596 - step 1439: {'train/loss': 0.036620140075683594, 'train/grad_norm': 0.649343729019165, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:42:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:42:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:42:57] train_sft.py:596 - step 1440: {'train/loss': 0.03777134418487549, 'train/grad_norm': 0.6207500696182251, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:42:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:42:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:01] train_sft.py:596 - step 1441: {'train/loss': 0.04187700152397156, 'train/grad_norm': 0.6974332928657532, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:43:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:43:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:06] train_sft.py:596 - step 1442: {'train/loss': 0.045508623123168945, 'train/grad_norm': 0.9224211573600769, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:43:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:43:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:11] train_sft.py:596 - step 1443: {'train/loss': 0.04306817054748535, 'train/grad_norm': 0.8152936697006226, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:43:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:43:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:15] train_sft.py:596 - step 1444: {'train/loss': 0.0314367413520813, 'train/grad_norm': 0.4690844416618347, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 13:43:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:43:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:20] train_sft.py:596 - step 1445: {'train/loss': 0.022595882415771484, 'train/grad_norm': 0.6526789665222168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:43:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:43:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:25] train_sft.py:596 - step 1446: {'train/loss': 0.041146159172058105, 'train/grad_norm': 0.871307373046875, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:43:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:43:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:30] train_sft.py:596 - step 1447: {'train/loss': 0.023883119225502014, 'train/grad_norm': 0.556860089302063, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:43:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:43:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:35] train_sft.py:596 - step 1448: {'train/loss': 0.036003947257995605, 'train/grad_norm': 0.5629541277885437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:43:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:43:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:40] train_sft.py:596 - step 1449: {'train/loss': 0.03217121958732605, 'train/grad_norm': 0.4948519766330719, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:43:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:43:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:44] train_sft.py:596 - step 1450: {'train/loss': 0.03184795379638672, 'train/grad_norm': 0.4483345150947571, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:43:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:43:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:49] train_sft.py:596 - step 1451: {'train/loss': 0.0294032022356987, 'train/grad_norm': 0.7283733487129211, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:43:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:43:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:54] train_sft.py:596 - step 1452: {'train/loss': 0.029892802238464355, 'train/grad_norm': 0.7942885756492615, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:43:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:43:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:43:58] train_sft.py:596 - step 1453: {'train/loss': 0.030831068754196167, 'train/grad_norm': 0.5510610342025757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 13:43:58] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:43:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:03] train_sft.py:596 - step 1454: {'train/loss': 0.023752272129058838, 'train/grad_norm': 0.6152231693267822, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:44:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:44:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:08] train_sft.py:596 - step 1455: {'train/loss': 0.031375885009765625, 'train/grad_norm': 0.6814723610877991, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:44:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:44:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:13] train_sft.py:596 - step 1456: {'train/loss': 0.02587917447090149, 'train/grad_norm': 0.39610084891319275, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:44:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:17] train_sft.py:596 - step 1457: {'train/loss': 0.024978995323181152, 'train/grad_norm': 0.6127644777297974, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:44:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:22] train_sft.py:596 - step 1458: {'train/loss': 0.0348820686340332, 'train/grad_norm': 0.5458760261535645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:44:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:27] train_sft.py:596 - step 1459: {'train/loss': 0.03438975848257542, 'train/grad_norm': 0.8701817393302917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:44:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:31] train_sft.py:596 - step 1460: {'train/loss': 0.04395103454589844, 'train/grad_norm': 1.3958094120025635, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:44:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:36] train_sft.py:596 - step 1461: {'train/loss': 0.03704547882080078, 'train/grad_norm': 0.5882558226585388, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:44:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:44:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:40] train_sft.py:596 - step 1462: {'train/loss': 0.02968454360961914, 'train/grad_norm': 0.8218072652816772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:44:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:45] train_sft.py:596 - step 1463: {'train/loss': 0.029625535011291504, 'train/grad_norm': 0.6235994100570679, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:44:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:44:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:50] train_sft.py:596 - step 1464: {'train/loss': 0.040746718645095825, 'train/grad_norm': 0.734366774559021, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:44:50] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:44:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:54] train_sft.py:596 - step 1465: {'train/loss': 0.050949543714523315, 'train/grad_norm': 0.7069844603538513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:44:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:44:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:44:59] train_sft.py:596 - step 1466: {'train/loss': 0.02319929003715515, 'train/grad_norm': 0.6657079458236694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:44:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:44:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:04] train_sft.py:596 - step 1467: {'train/loss': 0.03805851936340332, 'train/grad_norm': 0.7004765272140503, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:45:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:45:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:08] train_sft.py:596 - step 1468: {'train/loss': 0.03611034154891968, 'train/grad_norm': 0.9671316742897034, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 13:45:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:45:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:13] train_sft.py:596 - step 1469: {'train/loss': 0.058222949504852295, 'train/grad_norm': 0.8870381116867065, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:45:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:45:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:17] train_sft.py:596 - step 1470: {'train/loss': 0.04714608192443848, 'train/grad_norm': 0.8659688234329224, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:45:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:45:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:22] train_sft.py:596 - step 1471: {'train/loss': 0.03509410470724106, 'train/grad_norm': 0.596193790435791, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:45:22] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:45:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:27] train_sft.py:596 - step 1472: {'train/loss': 0.027301929891109467, 'train/grad_norm': 0.8721445202827454, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 13:45:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:45:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:32] train_sft.py:596 - step 1473: {'train/loss': 0.03613245487213135, 'train/grad_norm': 0.6584578156471252, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:45:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:45:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:37] train_sft.py:596 - step 1474: {'train/loss': 0.03256805241107941, 'train/grad_norm': 0.5829033851623535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:45:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:45:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:42] train_sft.py:596 - step 1475: {'train/loss': 0.03613834083080292, 'train/grad_norm': 0.6877937316894531, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 13:45:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:45:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:46] train_sft.py:596 - step 1476: {'train/loss': 0.026647835969924927, 'train/grad_norm': 0.5809087753295898, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:45:46] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:45:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:51] train_sft.py:596 - step 1477: {'train/loss': 0.04146003723144531, 'train/grad_norm': 0.4768233895301819, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:45:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:45:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:45:56] train_sft.py:596 - step 1478: {'train/loss': 0.040333449840545654, 'train/grad_norm': 0.8279799818992615, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:45:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:45:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:01] train_sft.py:596 - step 1479: {'train/loss': 0.041751429438591, 'train/grad_norm': 0.8520515561103821, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:46:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:46:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:05] train_sft.py:596 - step 1480: {'train/loss': 0.027487337589263916, 'train/grad_norm': 0.6093109846115112, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:46:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:10] train_sft.py:596 - step 1481: {'train/loss': 0.03022027015686035, 'train/grad_norm': 0.42825374007225037, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 13:46:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:46:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:14] train_sft.py:596 - step 1482: {'train/loss': 0.0456191748380661, 'train/grad_norm': 0.8788307905197144, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:46:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:19] train_sft.py:596 - step 1483: {'train/loss': 0.032939910888671875, 'train/grad_norm': 0.6132130026817322, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:46:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:24] train_sft.py:596 - step 1484: {'train/loss': 0.02937754988670349, 'train/grad_norm': 0.6796438694000244, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:46:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:46:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:29] train_sft.py:596 - step 1485: {'train/loss': 0.04156601428985596, 'train/grad_norm': 0.6045953035354614, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:46:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:33] train_sft.py:596 - step 1486: {'train/loss': 0.03068619966506958, 'train/grad_norm': 0.6928320527076721, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:46:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:38] train_sft.py:596 - step 1487: {'train/loss': 0.03462719917297363, 'train/grad_norm': 0.6971999406814575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 13:46:38] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:46:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:43] train_sft.py:596 - step 1488: {'train/loss': 0.04506707191467285, 'train/grad_norm': 0.9625294804573059, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:46:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:48] train_sft.py:596 - step 1489: {'train/loss': 0.05278852581977844, 'train/grad_norm': 1.0866681337356567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:46:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:46:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:53] train_sft.py:596 - step 1490: {'train/loss': 0.04371142387390137, 'train/grad_norm': 0.6635312438011169, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:46:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:46:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:46:58] train_sft.py:596 - step 1491: {'train/loss': 0.034690290689468384, 'train/grad_norm': 0.8207493424415588, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:46:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:46:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:02] train_sft.py:596 - step 1492: {'train/loss': 0.028376221656799316, 'train/grad_norm': 0.4747437536716461, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:47:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:47:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:07] train_sft.py:596 - step 1493: {'train/loss': 0.03463619947433472, 'train/grad_norm': 0.6441202163696289, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:47:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:47:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:12] train_sft.py:596 - step 1494: {'train/loss': 0.04407787322998047, 'train/grad_norm': 0.7629591822624207, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:47:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:47:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:16] train_sft.py:596 - step 1495: {'train/loss': 0.025057733058929443, 'train/grad_norm': 0.5840309858322144, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:47:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:47:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:21] train_sft.py:596 - step 1496: {'train/loss': 0.04276633262634277, 'train/grad_norm': 0.7127907872200012, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 13:47:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:47:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:26] train_sft.py:596 - step 1497: {'train/loss': 0.036208152770996094, 'train/grad_norm': 0.8913339972496033, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:47:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:47:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:31] train_sft.py:596 - step 1498: {'train/loss': 0.031661972403526306, 'train/grad_norm': 0.5446553826332092, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:47:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:47:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:47:35] train_sft.py:596 - step 1499: {'train/loss': 0.027997314929962158, 'train/grad_norm': 0.8021988272666931, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:47:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:01<02:58,  1.80s/it]
actor_val:   2%|▏         | 2/100 [00:03<02:43,  1.67s/it]
actor_val:   3%|▎         | 3/100 [00:04<02:37,  1.62s/it]
actor_val:   4%|▍         | 4/100 [00:06<02:33,  1.60s/it]
actor_val:   5%|▌         | 5/100 [00:08<02:43,  1.72s/it]
actor_val:   6%|▌         | 6/100 [00:10<02:36,  1.67s/it]
actor_val:   7%|▋         | 7/100 [00:11<02:33,  1.65s/it]
actor_val:   8%|▊         | 8/100 [00:13<02:29,  1.63s/it]
actor_val:   9%|▉         | 9/100 [00:15<02:34,  1.70s/it]
actor_val:  10%|█         | 10/100 [00:16<02:29,  1.67s/it]
actor_val:  11%|█         | 11/100 [00:18<02:24,  1.63s/it]
actor_val:  12%|█▏        | 12/100 [00:19<02:21,  1.60s/it]
actor_val:  13%|█▎        | 13/100 [00:21<02:23,  1.65s/it]
actor_val:  14%|█▍        | 14/100 [00:23<02:19,  1.62s/it]
actor_val:  15%|█▌        | 15/100 [00:24<02:16,  1.61s/it]
actor_val:  16%|█▌        | 16/100 [00:26<02:14,  1.60s/it]
actor_val:  17%|█▋        | 17/100 [00:27<02:11,  1.59s/it]
actor_val:  18%|█▊        | 18/100 [00:29<02:14,  1.64s/it]
actor_val:  19%|█▉        | 19/100 [00:31<02:12,  1.63s/it]
actor_val:  20%|██        | 20/100 [00:32<02:09,  1.61s/it]
actor_val:  21%|██        | 21/100 [00:34<02:12,  1.68s/it]
actor_val:  22%|██▏       | 22/100 [00:36<02:16,  1.75s/it]
actor_val:  23%|██▎       | 23/100 [00:38<02:10,  1.69s/it]
actor_val:  24%|██▍       | 24/100 [00:39<02:06,  1.66s/it]
actor_val:  25%|██▌       | 25/100 [00:41<02:07,  1.70s/it]
actor_val:  26%|██▌       | 26/100 [00:42<02:02,  1.66s/it]
actor_val:  27%|██▋       | 27/100 [00:44<01:59,  1.63s/it]
actor_val:  28%|██▊       | 28/100 [00:46<01:55,  1.60s/it]
actor_val:  29%|██▉       | 29/100 [00:47<01:52,  1.59s/it]
actor_val:  30%|███       | 30/100 [00:49<01:54,  1.64s/it]
actor_val:  31%|███       | 31/100 [00:50<01:51,  1.61s/it]
actor_val:  32%|███▏      | 32/100 [00:52<01:49,  1.61s/it]
actor_val:  33%|███▎      | 33/100 [00:54<01:46,  1.59s/it]
actor_val:  34%|███▍      | 34/100 [00:55<01:51,  1.69s/it]
actor_val:  35%|███▌      | 35/100 [00:57<01:53,  1.75s/it]
actor_val:  36%|███▌      | 36/100 [00:59<01:53,  1.77s/it]
actor_val:  37%|███▋      | 37/100 [01:01<01:47,  1.70s/it]
actor_val:  38%|███▊      | 38/100 [01:02<01:42,  1.66s/it]
actor_val:  39%|███▉      | 39/100 [01:04<01:39,  1.63s/it]
actor_val:  40%|████      | 40/100 [01:06<01:41,  1.70s/it]
actor_val:  41%|████      | 41/100 [01:08<01:42,  1.73s/it]
actor_val:  42%|████▏     | 42/100 [01:09<01:37,  1.69s/it]
actor_val:  43%|████▎     | 43/100 [01:11<01:38,  1.74s/it]
actor_val:  44%|████▍     | 44/100 [01:13<01:38,  1.77s/it]
actor_val:  45%|████▌     | 45/100 [01:14<01:33,  1.71s/it]
actor_val:  46%|████▌     | 46/100 [01:16<01:34,  1.74s/it]
actor_val:  47%|████▋     | 47/100 [01:18<01:29,  1.68s/it]
actor_val:  48%|████▊     | 48/100 [01:19<01:26,  1.66s/it]
actor_val:  49%|████▉     | 49/100 [01:21<01:27,  1.72s/it]
actor_val:  50%|█████     | 50/100 [01:23<01:23,  1.68s/it]
actor_val:  51%|█████     | 51/100 [01:24<01:20,  1.64s/it]
actor_val:  52%|█████▏    | 52/100 [01:26<01:20,  1.69s/it]
actor_val:  53%|█████▎    | 53/100 [01:28<01:17,  1.66s/it]
actor_val:  54%|█████▍    | 54/100 [01:29<01:15,  1.63s/it]
actor_val:  55%|█████▌    | 55/100 [01:31<01:16,  1.70s/it]
actor_val:  56%|█████▌    | 56/100 [01:33<01:12,  1.65s/it]
actor_val:  57%|█████▋    | 57/100 [01:35<01:13,  1.71s/it]
actor_val:  58%|█████▊    | 58/100 [01:36<01:10,  1.68s/it]
actor_val:  59%|█████▉    | 59/100 [01:38<01:07,  1.65s/it]
actor_val:  60%|██████    | 60/100 [01:40<01:08,  1.72s/it]
actor_val:  61%|██████    | 61/100 [01:41<01:04,  1.66s/it]
actor_val:  62%|██████▏   | 62/100 [01:43<01:02,  1.64s/it]
actor_val:  63%|██████▎   | 63/100 [01:44<01:00,  1.63s/it]
actor_val:  64%|██████▍   | 64/100 [01:46<01:01,  1.70s/it]
actor_val:  65%|██████▌   | 65/100 [01:48<01:00,  1.73s/it]
actor_val:  66%|██████▌   | 66/100 [01:50<00:59,  1.76s/it]
actor_val:  67%|██████▋   | 67/100 [01:51<00:55,  1.70s/it]
actor_val:  68%|██████▊   | 68/100 [01:53<00:52,  1.66s/it]
actor_val:  69%|██████▉   | 69/100 [01:55<00:50,  1.63s/it]
actor_val:  70%|███████   | 70/100 [01:56<00:50,  1.68s/it]
actor_val:  71%|███████   | 71/100 [01:58<00:47,  1.65s/it]
actor_val:  72%|███████▏  | 72/100 [01:59<00:45,  1.63s/it]
actor_val:  73%|███████▎  | 73/100 [02:01<00:46,  1.71s/it]
actor_val:  74%|███████▍  | 74/100 [02:03<00:45,  1.74s/it]
actor_val:  75%|███████▌  | 75/100 [02:05<00:44,  1.76s/it]
actor_val:  76%|███████▌  | 76/100 [02:07<00:40,  1.70s/it]
actor_val:  77%|███████▋  | 77/100 [02:08<00:40,  1.76s/it]
actor_val:  78%|███████▊  | 78/100 [02:10<00:39,  1.79s/it]
actor_val:  79%|███████▉  | 79/100 [02:12<00:38,  1.81s/it]
actor_val:  80%|████████  | 80/100 [02:14<00:36,  1.81s/it]
actor_val:  81%|████████  | 81/100 [02:16<00:33,  1.74s/it]
actor_val:  82%|████████▏ | 82/100 [02:17<00:30,  1.68s/it]
actor_val:  83%|████████▎ | 83/100 [02:19<00:27,  1.64s/it]
actor_val:  84%|████████▍ | 84/100 [02:21<00:27,  1.71s/it]
actor_val:  85%|████████▌ | 85/100 [02:22<00:25,  1.67s/it]
actor_val:  86%|████████▌ | 86/100 [02:24<00:23,  1.64s/it]
actor_val:  87%|████████▋ | 87/100 [02:26<00:22,  1.72s/it]
actor_val:  88%|████████▊ | 88/100 [02:27<00:21,  1.77s/it]
actor_val:  89%|████████▉ | 89/100 [02:29<00:18,  1.70s/it]
actor_val:  90%|█████████ | 90/100 [02:31<00:16,  1.66s/it]
actor_val:  91%|█████████ | 91/100 [02:32<00:15,  1.73s/it]
actor_val:  92%|█████████▏| 92/100 [02:34<00:14,  1.76s/it]
actor_val:  93%|█████████▎| 93/100 [02:36<00:12,  1.76s/it]
actor_val:  94%|█████████▍| 94/100 [02:38<00:10,  1.77s/it]
actor_val:  95%|█████████▌| 95/100 [02:40<00:08,  1.78s/it]
actor_val:  96%|█████████▌| 96/100 [02:41<00:07,  1.80s/it]
actor_val:  97%|█████████▋| 97/100 [02:43<00:05,  1.73s/it]
actor_val:  98%|█████████▊| 98/100 [02:45<00:03,  1.68s/it]
actor_val:  99%|█████████▉| 99/100 [02:46<00:01,  1.64s/it]
actor_val: 100%|██████████| 100/100 [02:48<00:00,  1.62s/it]
actor_val: 100%|██████████| 100/100 [02:48<00:00,  1.68s/it]
[2025-12-29 13:50:24] train_sft.py:658 - step 1500: {'val/loss': 0.3775}
[2025-12-29 13:50:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:29] train_sft.py:596 - step 1500: {'train/loss': 0.03803915157914162, 'train/grad_norm': 0.5834593772888184, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:50:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:50:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:34] train_sft.py:596 - step 1501: {'train/loss': 0.029869139194488525, 'train/grad_norm': 0.6449307799339294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:50:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:50:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:39] train_sft.py:596 - step 1502: {'train/loss': 0.03572291135787964, 'train/grad_norm': 0.5102441310882568, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:50:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:50:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:43] train_sft.py:596 - step 1503: {'train/loss': 0.03878593444824219, 'train/grad_norm': 0.6239860653877258, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:50:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:50:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:48] train_sft.py:596 - step 1504: {'train/loss': 0.044182777404785156, 'train/grad_norm': 0.7921897768974304, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:50:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:50:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:53] train_sft.py:596 - step 1505: {'train/loss': 0.03745549917221069, 'train/grad_norm': 0.6212327480316162, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:50:53] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:50:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:50:57] train_sft.py:596 - step 1506: {'train/loss': 0.04182806611061096, 'train/grad_norm': 0.5619315505027771, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:50:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:50:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:02] train_sft.py:596 - step 1507: {'train/loss': 0.034763336181640625, 'train/grad_norm': 0.5456678867340088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:51:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:51:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:07] train_sft.py:596 - step 1508: {'train/loss': 0.030052468180656433, 'train/grad_norm': 0.5890616178512573, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:51:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:51:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:12] train_sft.py:596 - step 1509: {'train/loss': 0.038196563720703125, 'train/grad_norm': 0.5824062824249268, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:51:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:51:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:17] train_sft.py:596 - step 1510: {'train/loss': 0.030630826950073242, 'train/grad_norm': 0.44331449270248413, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:51:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:51:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:21] train_sft.py:596 - step 1511: {'train/loss': 0.02676403522491455, 'train/grad_norm': 0.4235207736492157, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:51:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:51:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:26] train_sft.py:596 - step 1512: {'train/loss': 0.04101157188415527, 'train/grad_norm': 0.48238813877105713, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 13:51:26] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:51:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:31] train_sft.py:596 - step 1513: {'train/loss': 0.04478856921195984, 'train/grad_norm': 0.7681444883346558, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 13:51:31] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:51:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:36] train_sft.py:596 - step 1514: {'train/loss': 0.033831238746643066, 'train/grad_norm': 0.7477670907974243, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:51:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:51:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:41] train_sft.py:596 - step 1515: {'train/loss': 0.03548477590084076, 'train/grad_norm': 0.4486404359340668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 13:51:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:51:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:46] train_sft.py:596 - step 1516: {'train/loss': 0.029022306203842163, 'train/grad_norm': 0.5252747535705566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:51:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:51:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:51] train_sft.py:596 - step 1517: {'train/loss': 0.03597384691238403, 'train/grad_norm': 0.7953988313674927, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:51:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:51:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:51:56] train_sft.py:596 - step 1518: {'train/loss': 0.04084520787000656, 'train/grad_norm': 0.7881389260292053, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 13:51:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:51:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:01] train_sft.py:596 - step 1519: {'train/loss': 0.038501203060150146, 'train/grad_norm': 0.6741310358047485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 13:52:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:52:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:06] train_sft.py:596 - step 1520: {'train/loss': 0.03463160991668701, 'train/grad_norm': 0.5017467737197876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 13:52:06] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:52:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:11] train_sft.py:596 - step 1521: {'train/loss': 0.04016721248626709, 'train/grad_norm': 0.7833118438720703, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 13:52:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:52:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:16] train_sft.py:596 - step 1522: {'train/loss': 0.031312018632888794, 'train/grad_norm': 0.6126201748847961, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 13:52:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:52:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:20] train_sft.py:596 - step 1523: {'train/loss': 0.04418748617172241, 'train/grad_norm': 0.7193255424499512, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:52:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:52:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:26] train_sft.py:596 - step 1524: {'train/loss': 0.03490576148033142, 'train/grad_norm': 0.8775686025619507, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 13:52:26] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 13:52:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:31] train_sft.py:596 - step 1525: {'train/loss': 0.04181142896413803, 'train/grad_norm': 0.809307336807251, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:52:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:52:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:35] train_sft.py:596 - step 1526: {'train/loss': 0.033888161182403564, 'train/grad_norm': 0.492498517036438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:52:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:52:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:40] train_sft.py:596 - step 1527: {'train/loss': 0.028784334659576416, 'train/grad_norm': 0.5301294326782227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 13:52:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:52:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:45] train_sft.py:596 - step 1528: {'train/loss': 0.0369257926940918, 'train/grad_norm': 0.6686094403266907, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:52:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:52:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:50] train_sft.py:596 - step 1529: {'train/loss': 0.0335911363363266, 'train/grad_norm': 0.5871270895004272, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:52:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:52:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:55] train_sft.py:596 - step 1530: {'train/loss': 0.035756587982177734, 'train/grad_norm': 0.7026354074478149, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 13:52:55] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:52:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:52:59] train_sft.py:596 - step 1531: {'train/loss': 0.03370511531829834, 'train/grad_norm': 0.5158427953720093, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:52:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:53:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:04] train_sft.py:596 - step 1532: {'train/loss': 0.03901660442352295, 'train/grad_norm': 0.4564148783683777, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:53:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:53:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:09] train_sft.py:596 - step 1533: {'train/loss': 0.057161569595336914, 'train/grad_norm': 0.9280438423156738, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:53:09] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:53:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:14] train_sft.py:596 - step 1534: {'train/loss': 0.032391905784606934, 'train/grad_norm': 0.34935447573661804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 13:53:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:53:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:18] train_sft.py:596 - step 1535: {'train/loss': 0.04790306091308594, 'train/grad_norm': 1.0384690761566162, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 13:53:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:53:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:23] train_sft.py:596 - step 1536: {'train/loss': 0.03186321258544922, 'train/grad_norm': 0.42768236994743347, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:53:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:53:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:28] train_sft.py:596 - step 1537: {'train/loss': 0.03249382972717285, 'train/grad_norm': 0.5992681384086609, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:53:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:53:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:33] train_sft.py:596 - step 1538: {'train/loss': 0.03916823863983154, 'train/grad_norm': 0.6139651536941528, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 13:53:33] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:53:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:38] train_sft.py:596 - step 1539: {'train/loss': 0.036923885345458984, 'train/grad_norm': 0.49477919936180115, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 13:53:38] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 13:53:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:43] train_sft.py:596 - step 1540: {'train/loss': 0.043629273772239685, 'train/grad_norm': 0.7317051291465759, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:53:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:53:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:47] train_sft.py:596 - step 1541: {'train/loss': 0.03476780652999878, 'train/grad_norm': 0.6913521885871887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:53:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:53:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:53] train_sft.py:596 - step 1542: {'train/loss': 0.0376507043838501, 'train/grad_norm': 0.6496753692626953, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 13:53:53] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:53:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:53:58] train_sft.py:596 - step 1543: {'train/loss': 0.034201085567474365, 'train/grad_norm': 0.520389974117279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:53:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:53:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:02] train_sft.py:596 - step 1544: {'train/loss': 0.03845930099487305, 'train/grad_norm': 0.5550721883773804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:54:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:07] train_sft.py:596 - step 1545: {'train/loss': 0.03659653663635254, 'train/grad_norm': 0.7077206373214722, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:54:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:11] train_sft.py:596 - step 1546: {'train/loss': 0.02848231792449951, 'train/grad_norm': 0.37508100271224976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:54:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:16] train_sft.py:596 - step 1547: {'train/loss': 0.04186701029539108, 'train/grad_norm': 0.7867854237556458, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:54:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:21] train_sft.py:596 - step 1548: {'train/loss': 0.03196370601654053, 'train/grad_norm': 0.5303744077682495, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:54:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:54:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:26] train_sft.py:596 - step 1549: {'train/loss': 0.04200553894042969, 'train/grad_norm': 0.9071788191795349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 13:54:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:54:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:30] train_sft.py:596 - step 1550: {'train/loss': 0.03716754913330078, 'train/grad_norm': 0.641494631767273, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 13:54:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:35] train_sft.py:596 - step 1551: {'train/loss': 0.04449427127838135, 'train/grad_norm': 0.8006302118301392, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:54:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:40] train_sft.py:596 - step 1552: {'train/loss': 0.03127264976501465, 'train/grad_norm': 0.9898691773414612, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:54:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:44] train_sft.py:596 - step 1553: {'train/loss': 0.044144466519355774, 'train/grad_norm': 0.7699110507965088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:54:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:49] train_sft.py:596 - step 1554: {'train/loss': 0.03829360008239746, 'train/grad_norm': 0.5507797598838806, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:54:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:54] train_sft.py:596 - step 1555: {'train/loss': 0.03279387950897217, 'train/grad_norm': 0.7676411867141724, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:54:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:54:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:54:59] train_sft.py:596 - step 1556: {'train/loss': 0.03246474266052246, 'train/grad_norm': 0.5225365161895752, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 13:54:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:54:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:03] train_sft.py:596 - step 1557: {'train/loss': 0.04107198119163513, 'train/grad_norm': 0.8776324987411499, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:55:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:55:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:08] train_sft.py:596 - step 1558: {'train/loss': 0.04310965538024902, 'train/grad_norm': 0.7486030459403992, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:55:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:13] train_sft.py:596 - step 1559: {'train/loss': 0.033370018005371094, 'train/grad_norm': 0.8003357648849487, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 13:55:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:17] train_sft.py:596 - step 1560: {'train/loss': 0.0372697114944458, 'train/grad_norm': 0.6702005863189697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:55:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:22] train_sft.py:596 - step 1561: {'train/loss': 0.03718984127044678, 'train/grad_norm': 0.6512524485588074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:55:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:26] train_sft.py:596 - step 1562: {'train/loss': 0.034163862466812134, 'train/grad_norm': 0.739732563495636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:55:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:55:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:31] train_sft.py:596 - step 1563: {'train/loss': 0.04195737838745117, 'train/grad_norm': 0.6981107592582703, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:55:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:55:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:36] train_sft.py:596 - step 1564: {'train/loss': 0.03578662872314453, 'train/grad_norm': 0.6682714223861694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:55:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:41] train_sft.py:596 - step 1565: {'train/loss': 0.03205239772796631, 'train/grad_norm': 0.5917197465896606, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:55:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:45] train_sft.py:596 - step 1566: {'train/loss': 0.045049190521240234, 'train/grad_norm': 0.8714262247085571, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:55:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:50] train_sft.py:596 - step 1567: {'train/loss': 0.03688827157020569, 'train/grad_norm': 0.8658382296562195, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:55:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:55:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:55:55] train_sft.py:596 - step 1568: {'train/loss': 0.03256845474243164, 'train/grad_norm': 0.7641676068305969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 13:55:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:55:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:00] train_sft.py:596 - step 1569: {'train/loss': 0.032157182693481445, 'train/grad_norm': 0.557643711566925, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 13:56:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:56:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:04] train_sft.py:596 - step 1570: {'train/loss': 0.03393661975860596, 'train/grad_norm': 0.631168007850647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:56:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:09] train_sft.py:596 - step 1571: {'train/loss': 0.043796882033348083, 'train/grad_norm': 0.5928024649620056, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:56:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:14] train_sft.py:596 - step 1572: {'train/loss': 0.04283952713012695, 'train/grad_norm': 0.5816812515258789, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:56:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:18] train_sft.py:596 - step 1573: {'train/loss': 0.03862476348876953, 'train/grad_norm': 0.6911500096321106, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:56:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:23] train_sft.py:596 - step 1574: {'train/loss': 0.04055500030517578, 'train/grad_norm': 0.6943206191062927, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:56:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:28] train_sft.py:596 - step 1575: {'train/loss': 0.03033539652824402, 'train/grad_norm': 0.3906995952129364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:56:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:33] train_sft.py:596 - step 1576: {'train/loss': 0.026328444480895996, 'train/grad_norm': 0.5381448864936829, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 13:56:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:56:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:37] train_sft.py:596 - step 1577: {'train/loss': 0.02238735556602478, 'train/grad_norm': 0.3432934582233429, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:56:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:42] train_sft.py:596 - step 1578: {'train/loss': 0.022283941507339478, 'train/grad_norm': 0.39553776383399963, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:56:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:47] train_sft.py:596 - step 1579: {'train/loss': 0.029294312000274658, 'train/grad_norm': 0.8055400848388672, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:56:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:51] train_sft.py:596 - step 1580: {'train/loss': 0.0264873206615448, 'train/grad_norm': 0.629244863986969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:56:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:56:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:56:56] train_sft.py:596 - step 1581: {'train/loss': 0.01725296676158905, 'train/grad_norm': 0.6298810839653015, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:56:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:56:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:01] train_sft.py:596 - step 1582: {'train/loss': 0.017557770013809204, 'train/grad_norm': 0.4836030900478363, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 13:57:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:57:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:05] train_sft.py:596 - step 1583: {'train/loss': 0.02304452657699585, 'train/grad_norm': 0.6321648955345154, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 13:57:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:57:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:10] train_sft.py:596 - step 1584: {'train/loss': 0.026700496673583984, 'train/grad_norm': 0.8727383017539978, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 13:57:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:57:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:15] train_sft.py:596 - step 1585: {'train/loss': 0.031181901693344116, 'train/grad_norm': 0.7915344834327698, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 13:57:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:57:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:20] train_sft.py:596 - step 1586: {'train/loss': 0.023727774620056152, 'train/grad_norm': 0.5369185209274292, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:57:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:57:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:24] train_sft.py:596 - step 1587: {'train/loss': 0.020284831523895264, 'train/grad_norm': 0.4394591450691223, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 13:57:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:57:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:29] train_sft.py:596 - step 1588: {'train/loss': 0.0182819664478302, 'train/grad_norm': 0.4965342581272125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 13:57:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:57:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:34] train_sft.py:596 - step 1589: {'train/loss': 0.022267460823059082, 'train/grad_norm': 0.4378926157951355, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:57:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:57:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:38] train_sft.py:596 - step 1590: {'train/loss': 0.020799756050109863, 'train/grad_norm': 0.6528408527374268, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 13:57:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:57:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:43] train_sft.py:596 - step 1591: {'train/loss': 0.021470844745635986, 'train/grad_norm': 0.4594563841819763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 13:57:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:57:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:48] train_sft.py:596 - step 1592: {'train/loss': 0.019290655851364136, 'train/grad_norm': 0.5745669603347778, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:57:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:57:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:52] train_sft.py:596 - step 1593: {'train/loss': 0.03322617709636688, 'train/grad_norm': 0.6422383785247803, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:57:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:57:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:57:57] train_sft.py:596 - step 1594: {'train/loss': 0.022794097661972046, 'train/grad_norm': 0.8325762748718262, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:57:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:57:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:02] train_sft.py:596 - step 1595: {'train/loss': 0.027528300881385803, 'train/grad_norm': 0.5610256791114807, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 13:58:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:58:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:07] train_sft.py:596 - step 1596: {'train/loss': 0.025207653641700745, 'train/grad_norm': 1.1586391925811768, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 13:58:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:58:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:11] train_sft.py:596 - step 1597: {'train/loss': 0.032103896141052246, 'train/grad_norm': 0.620471715927124, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:58:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:58:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:16] train_sft.py:596 - step 1598: {'train/loss': 0.021414577960968018, 'train/grad_norm': 0.5466559529304504, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 13:58:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:58:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:21] train_sft.py:596 - step 1599: {'train/loss': 0.022401273250579834, 'train/grad_norm': 0.6113110780715942, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:58:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:58:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:26] train_sft.py:596 - step 1600: {'train/loss': 0.02217632532119751, 'train/grad_norm': 0.4224517345428467, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 13:58:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:58:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:30] train_sft.py:596 - step 1601: {'train/loss': 0.021395087242126465, 'train/grad_norm': 0.4910447299480438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:58:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:58:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:35] train_sft.py:596 - step 1602: {'train/loss': 0.02498149871826172, 'train/grad_norm': 0.6992406845092773, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 13:58:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:58:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:40] train_sft.py:596 - step 1603: {'train/loss': 0.026440978050231934, 'train/grad_norm': 0.5856034159660339, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:58:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:58:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:45] train_sft.py:596 - step 1604: {'train/loss': 0.02785840630531311, 'train/grad_norm': 0.6464176177978516, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 13:58:45] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 13:58:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:49] train_sft.py:596 - step 1605: {'train/loss': 0.02170051634311676, 'train/grad_norm': 0.4076845347881317, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 13:58:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:58:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:54] train_sft.py:596 - step 1606: {'train/loss': 0.0286715030670166, 'train/grad_norm': 0.8279914855957031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 13:58:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:58:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:58:59] train_sft.py:596 - step 1607: {'train/loss': 0.02096758410334587, 'train/grad_norm': 0.46871063113212585, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:58:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:58:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:03] train_sft.py:596 - step 1608: {'train/loss': 0.02406024932861328, 'train/grad_norm': 0.4989764392375946, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 13:59:03] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 13:59:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:08] train_sft.py:596 - step 1609: {'train/loss': 0.026800811290740967, 'train/grad_norm': 0.8077492713928223, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 13:59:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:59:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:13] train_sft.py:596 - step 1610: {'train/loss': 0.023190215229988098, 'train/grad_norm': 0.5119836330413818, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 13:59:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:59:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:17] train_sft.py:596 - step 1611: {'train/loss': 0.030075362883508205, 'train/grad_norm': 0.6394291520118713, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 13:59:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:59:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:22] train_sft.py:596 - step 1612: {'train/loss': 0.025609493255615234, 'train/grad_norm': 0.707318902015686, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 13:59:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 13:59:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:27] train_sft.py:596 - step 1613: {'train/loss': 0.020075500011444092, 'train/grad_norm': 0.5412954092025757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:59:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:59:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:31] train_sft.py:596 - step 1614: {'train/loss': 0.030221164226531982, 'train/grad_norm': 0.6774114370346069, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 13:59:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:59:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:36] train_sft.py:596 - step 1615: {'train/loss': 0.025406301021575928, 'train/grad_norm': 0.8012161254882812, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 13:59:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:59:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:41] train_sft.py:596 - step 1616: {'train/loss': 0.01846235990524292, 'train/grad_norm': 0.3492974042892456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 13:59:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 13:59:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:46] train_sft.py:596 - step 1617: {'train/loss': 0.02700817584991455, 'train/grad_norm': 0.5088728070259094, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 13:59:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 13:59:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:51] train_sft.py:596 - step 1618: {'train/loss': 0.019762277603149414, 'train/grad_norm': 0.38783755898475647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 13:59:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 13:59:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 13:59:56] train_sft.py:596 - step 1619: {'train/loss': 0.021895498037338257, 'train/grad_norm': 0.4591566026210785, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 13:59:56] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 13:59:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:01] train_sft.py:596 - step 1620: {'train/loss': 0.0692858025431633, 'train/grad_norm': 1.054366946220398, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:00:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:00:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:05] train_sft.py:596 - step 1621: {'train/loss': 0.02445038966834545, 'train/grad_norm': 0.7355719208717346, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:00:05] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:00:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:10] train_sft.py:596 - step 1622: {'train/loss': 0.018616892397403717, 'train/grad_norm': 0.5315456390380859, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:00:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:00:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:15] train_sft.py:596 - step 1623: {'train/loss': 0.022485122084617615, 'train/grad_norm': 0.46360430121421814, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:00:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:00:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:19] train_sft.py:596 - step 1624: {'train/loss': 0.029770880937576294, 'train/grad_norm': 0.6420477032661438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:00:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:00:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:24] train_sft.py:596 - step 1625: {'train/loss': 0.02105391025543213, 'train/grad_norm': 0.5088520646095276, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:00:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:00:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:29] train_sft.py:596 - step 1626: {'train/loss': 0.03468960523605347, 'train/grad_norm': 0.7704817652702332, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:00:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:00:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:34] train_sft.py:596 - step 1627: {'train/loss': 0.024179425090551376, 'train/grad_norm': 0.789641797542572, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 14:00:34] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:00:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:39] train_sft.py:596 - step 1628: {'train/loss': 0.020200654864311218, 'train/grad_norm': 0.4424363374710083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:00:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:00:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:43] train_sft.py:596 - step 1629: {'train/loss': 0.02274864912033081, 'train/grad_norm': 0.45077580213546753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:00:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:00:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:48] train_sft.py:596 - step 1630: {'train/loss': 0.03373399376869202, 'train/grad_norm': 0.6680535674095154, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:00:48] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:00:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:53] train_sft.py:596 - step 1631: {'train/loss': 0.02775600552558899, 'train/grad_norm': 0.6077046990394592, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:00:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:00:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:00:58] train_sft.py:596 - step 1632: {'train/loss': 0.021256044507026672, 'train/grad_norm': 0.4381251335144043, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 14:00:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:00:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:03] train_sft.py:596 - step 1633: {'train/loss': 0.017590075731277466, 'train/grad_norm': 0.5388889908790588, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 14:01:03] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:01:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:08] train_sft.py:596 - step 1634: {'train/loss': 0.02273261547088623, 'train/grad_norm': 0.47910431027412415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:01:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:01:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:12] train_sft.py:596 - step 1635: {'train/loss': 0.029466569423675537, 'train/grad_norm': 0.6573729515075684, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:01:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:01:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:17] train_sft.py:596 - step 1636: {'train/loss': 0.01676154136657715, 'train/grad_norm': 0.35756614804267883, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:01:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:01:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:21] train_sft.py:596 - step 1637: {'train/loss': 0.03208870440721512, 'train/grad_norm': 0.5204894542694092, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:01:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:01:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:26] train_sft.py:596 - step 1638: {'train/loss': 0.035726189613342285, 'train/grad_norm': 0.6836289763450623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:01:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:01:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:31] train_sft.py:596 - step 1639: {'train/loss': 0.03125464916229248, 'train/grad_norm': 0.4663110673427582, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 14:01:31] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:01:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:36] train_sft.py:596 - step 1640: {'train/loss': 0.019072353839874268, 'train/grad_norm': 0.39207136631011963, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 14:01:36] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:01:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:41] train_sft.py:596 - step 1641: {'train/loss': 0.023540874011814594, 'train/grad_norm': 0.6290466785430908, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:01:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:01:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:45] train_sft.py:596 - step 1642: {'train/loss': 0.03188788890838623, 'train/grad_norm': 0.8714848756790161, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:01:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:01:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:50] train_sft.py:596 - step 1643: {'train/loss': 0.034173935651779175, 'train/grad_norm': 0.5927199721336365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:01:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:01:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:01:55] train_sft.py:596 - step 1644: {'train/loss': 0.023785829544067383, 'train/grad_norm': 0.44607454538345337, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:01:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:01:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:00] train_sft.py:596 - step 1645: {'train/loss': 0.020203471183776855, 'train/grad_norm': 0.6610302925109863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:02:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:02:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:05] train_sft.py:596 - step 1646: {'train/loss': 0.02483507990837097, 'train/grad_norm': 0.47047752141952515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:02:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:02:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:10] train_sft.py:596 - step 1647: {'train/loss': 0.02594161033630371, 'train/grad_norm': 0.7677865624427795, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 14:02:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:02:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:15] train_sft.py:596 - step 1648: {'train/loss': 0.024414420127868652, 'train/grad_norm': 0.6395359635353088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:02:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:02:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:20] train_sft.py:596 - step 1649: {'train/loss': 0.03463186323642731, 'train/grad_norm': 0.5067200660705566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
[2025-12-29 14:02:20] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:02:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:24] train_sft.py:596 - step 1650: {'train/loss': 0.025813966989517212, 'train/grad_norm': 0.5700990557670593, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:02:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:02:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:29] train_sft.py:596 - step 1651: {'train/loss': 0.017686236649751663, 'train/grad_norm': 0.352218359708786, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:02:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:02:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:34] train_sft.py:596 - step 1652: {'train/loss': 0.026362324133515358, 'train/grad_norm': 0.7595426440238953, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:02:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:02:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:39] train_sft.py:596 - step 1653: {'train/loss': 0.027225613594055176, 'train/grad_norm': 0.8013982772827148, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:02:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:02:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:44] train_sft.py:596 - step 1654: {'train/loss': 0.03523528575897217, 'train/grad_norm': 0.6148358583450317, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:02:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:02:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:49] train_sft.py:596 - step 1655: {'train/loss': 0.023524463176727295, 'train/grad_norm': 0.5345098376274109, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 14:02:49] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:02:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:53] train_sft.py:596 - step 1656: {'train/loss': 0.03155834972858429, 'train/grad_norm': 0.5443357825279236, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:02:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:02:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:02:58] train_sft.py:596 - step 1657: {'train/loss': 0.031189776957035065, 'train/grad_norm': 0.7331572771072388, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:02:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:02:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:04] train_sft.py:596 - step 1658: {'train/loss': 0.0304945707321167, 'train/grad_norm': 0.773280143737793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 14:03:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:03:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:08] train_sft.py:596 - step 1659: {'train/loss': 0.03827577829360962, 'train/grad_norm': 0.7727445960044861, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:03:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:03:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:13] train_sft.py:596 - step 1660: {'train/loss': 0.034886062145233154, 'train/grad_norm': 0.6986079216003418, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:03:13] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:03:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:18] train_sft.py:596 - step 1661: {'train/loss': 0.027986363507807255, 'train/grad_norm': 0.8212437629699707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:03:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:03:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:23] train_sft.py:596 - step 1662: {'train/loss': 0.03240978717803955, 'train/grad_norm': 0.8379715085029602, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:03:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:03:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:28] train_sft.py:596 - step 1663: {'train/loss': 0.03548264503479004, 'train/grad_norm': 0.7149069309234619, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:03:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:03:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:32] train_sft.py:596 - step 1664: {'train/loss': 0.021834850311279297, 'train/grad_norm': 0.36300379037857056, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 14:03:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:03:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:37] train_sft.py:596 - step 1665: {'train/loss': 0.027919545769691467, 'train/grad_norm': 0.6208043098449707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 14:03:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:03:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:42] train_sft.py:596 - step 1666: {'train/loss': 0.03148306906223297, 'train/grad_norm': 0.5847036242485046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:03:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:03:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:47] train_sft.py:596 - step 1667: {'train/loss': 0.024516522884368896, 'train/grad_norm': 0.5272499322891235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:03:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:03:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:52] train_sft.py:596 - step 1668: {'train/loss': 0.03111022710800171, 'train/grad_norm': 0.43602895736694336, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:03:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:03:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:03:57] train_sft.py:596 - step 1669: {'train/loss': 0.030833125114440918, 'train/grad_norm': 0.6890157461166382, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:03:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:03:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:02] train_sft.py:596 - step 1670: {'train/loss': 0.019830025732517242, 'train/grad_norm': 0.5356335043907166, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 14:04:02] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:04:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:07] train_sft.py:596 - step 1671: {'train/loss': 0.03631782531738281, 'train/grad_norm': 0.5572127103805542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:04:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:04:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:12] train_sft.py:596 - step 1672: {'train/loss': 0.027149438858032227, 'train/grad_norm': 0.4765082895755768, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:04:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:04:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:17] train_sft.py:596 - step 1673: {'train/loss': 0.03333902359008789, 'train/grad_norm': 0.6650410294532776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 14:04:17] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:04:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:22] train_sft.py:596 - step 1674: {'train/loss': 0.030826687812805176, 'train/grad_norm': 0.627488911151886, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 14:04:22] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:04:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:26] train_sft.py:596 - step 1675: {'train/loss': 0.02401888370513916, 'train/grad_norm': 0.5104479789733887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:04:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:04:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:32] train_sft.py:596 - step 1676: {'train/loss': 0.03216094523668289, 'train/grad_norm': 0.9253814220428467, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 14:04:32] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:04:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:37] train_sft.py:596 - step 1677: {'train/loss': 0.02418208122253418, 'train/grad_norm': 0.7095576524734497, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 14:04:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:04:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:42] train_sft.py:596 - step 1678: {'train/loss': 0.024743974208831787, 'train/grad_norm': 0.6777032017707825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 14:04:42] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:04:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:47] train_sft.py:596 - step 1679: {'train/loss': 0.022264599800109863, 'train/grad_norm': 0.4270334839820862, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:04:47] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:04:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:52] train_sft.py:596 - step 1680: {'train/loss': 0.028032392263412476, 'train/grad_norm': 0.5140309929847717, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 14:04:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:04:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:04:57] train_sft.py:596 - step 1681: {'train/loss': 0.030932247638702393, 'train/grad_norm': 0.5326048135757446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:04:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:04:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:02] train_sft.py:596 - step 1682: {'train/loss': 0.026128798723220825, 'train/grad_norm': 0.44622188806533813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:05:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:05:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:06] train_sft.py:596 - step 1683: {'train/loss': 0.029304981231689453, 'train/grad_norm': 0.5200228691101074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:05:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:05:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:11] train_sft.py:596 - step 1684: {'train/loss': 0.030163347721099854, 'train/grad_norm': 0.5674329400062561, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:05:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:05:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:16] train_sft.py:596 - step 1685: {'train/loss': 0.02844405174255371, 'train/grad_norm': 0.6574283242225647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:05:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:05:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:21] train_sft.py:596 - step 1686: {'train/loss': 0.02557721734046936, 'train/grad_norm': 0.5238673090934753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:05:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:05:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:25] train_sft.py:596 - step 1687: {'train/loss': 0.02858734130859375, 'train/grad_norm': 0.6808600425720215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:05:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:05:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:30] train_sft.py:596 - step 1688: {'train/loss': 0.03408777713775635, 'train/grad_norm': 0.6390597820281982, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:05:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:05:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:35] train_sft.py:596 - step 1689: {'train/loss': 0.029877007007598877, 'train/grad_norm': 0.5555763840675354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
[2025-12-29 14:05:35] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:05:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:40] train_sft.py:596 - step 1690: {'train/loss': 0.022864341735839844, 'train/grad_norm': 0.477324515581131, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:05:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:05:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:45] train_sft.py:596 - step 1691: {'train/loss': 0.026528537273406982, 'train/grad_norm': 0.6740511059761047, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 14:05:45] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:05:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:50] train_sft.py:596 - step 1692: {'train/loss': 0.025401651859283447, 'train/grad_norm': 0.556598424911499, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:05:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:05:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:55] train_sft.py:596 - step 1693: {'train/loss': 0.03389793634414673, 'train/grad_norm': 0.6496230363845825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:05:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:05:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:05:59] train_sft.py:596 - step 1694: {'train/loss': 0.033333778381347656, 'train/grad_norm': 0.8232262134552002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 14:05:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:06:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:04] train_sft.py:596 - step 1695: {'train/loss': 0.022905247285962105, 'train/grad_norm': 0.6483498215675354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:06:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:06:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:09] train_sft.py:596 - step 1696: {'train/loss': 0.030521154403686523, 'train/grad_norm': 0.5034375190734863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 14:06:09] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:06:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:14] train_sft.py:596 - step 1697: {'train/loss': 0.025030136108398438, 'train/grad_norm': 0.7055130004882812, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 14:06:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:06:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:18] train_sft.py:596 - step 1698: {'train/loss': 0.02466540038585663, 'train/grad_norm': 0.5622413754463196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:06:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:06:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:23] train_sft.py:596 - step 1699: {'train/loss': 0.030708909034729004, 'train/grad_norm': 0.5918684601783752, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:06:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:06:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:28] train_sft.py:596 - step 1700: {'train/loss': 0.029033944010734558, 'train/grad_norm': 0.6255589723587036, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:06:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:06:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:33] train_sft.py:596 - step 1701: {'train/loss': 0.021942807361483574, 'train/grad_norm': 0.38262102007865906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:06:33] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:06:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:38] train_sft.py:596 - step 1702: {'train/loss': 0.03397071361541748, 'train/grad_norm': 0.5429700613021851, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 14:06:38] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:06:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:43] train_sft.py:596 - step 1703: {'train/loss': 0.029760003089904785, 'train/grad_norm': 0.7312731742858887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:06:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:06:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:48] train_sft.py:596 - step 1704: {'train/loss': 0.024884790182113647, 'train/grad_norm': 0.4296642243862152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:06:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:06:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:53] train_sft.py:596 - step 1705: {'train/loss': 0.027986332774162292, 'train/grad_norm': 0.6003636717796326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
[2025-12-29 14:06:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:06:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:06:58] train_sft.py:596 - step 1706: {'train/loss': 0.03852248191833496, 'train/grad_norm': 0.5580400228500366, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:06:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:06:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:03] train_sft.py:596 - step 1707: {'train/loss': 0.02663809061050415, 'train/grad_norm': 0.5605120658874512, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:07:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:07:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:07] train_sft.py:596 - step 1708: {'train/loss': 0.028166651725769043, 'train/grad_norm': 0.4642695188522339, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:07:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:07:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:12] train_sft.py:596 - step 1709: {'train/loss': 0.031276047229766846, 'train/grad_norm': 0.4957364499568939, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:07:12] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:07:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:17] train_sft.py:596 - step 1710: {'train/loss': 0.030636906623840332, 'train/grad_norm': 0.7680893540382385, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:07:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:07:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:21] train_sft.py:596 - step 1711: {'train/loss': 0.023777306079864502, 'train/grad_norm': 0.5221002697944641, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:07:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:07:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:26] train_sft.py:596 - step 1712: {'train/loss': 0.027155041694641113, 'train/grad_norm': 0.7462083697319031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 14:07:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:07:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:31] train_sft.py:596 - step 1713: {'train/loss': 0.02224433422088623, 'train/grad_norm': 0.40697136521339417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:07:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:07:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:35] train_sft.py:596 - step 1714: {'train/loss': 0.03673384943976998, 'train/grad_norm': 0.677276611328125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:07:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:07:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:40] train_sft.py:596 - step 1715: {'train/loss': 0.027238011360168457, 'train/grad_norm': 0.5294549465179443, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:07:40] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:07:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:45] train_sft.py:596 - step 1716: {'train/loss': 0.03131818771362305, 'train/grad_norm': 0.6955824494361877, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:07:45] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:07:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:50] train_sft.py:596 - step 1717: {'train/loss': 0.026423249393701553, 'train/grad_norm': 0.8366697430610657, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:07:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:07:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:07:55] train_sft.py:596 - step 1718: {'train/loss': 0.032084375619888306, 'train/grad_norm': 0.8381204605102539, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:07:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:07:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:00] train_sft.py:596 - step 1719: {'train/loss': 0.02886509895324707, 'train/grad_norm': 0.7487219572067261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:08:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:08:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:04] train_sft.py:596 - step 1720: {'train/loss': 0.031241893768310547, 'train/grad_norm': 0.7086532115936279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:08:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:08:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:09] train_sft.py:596 - step 1721: {'train/loss': 0.031389474868774414, 'train/grad_norm': 1.0961062908172607, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:08:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:08:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:14] train_sft.py:596 - step 1722: {'train/loss': 0.025707364082336426, 'train/grad_norm': 0.7741106748580933, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 14:08:14] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:08:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:19] train_sft.py:596 - step 1723: {'train/loss': 0.02402481436729431, 'train/grad_norm': 0.5930871963500977, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 14:08:19] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:08:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:23] train_sft.py:596 - step 1724: {'train/loss': 0.026520073413848877, 'train/grad_norm': 0.8420795798301697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:08:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:08:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:28] train_sft.py:596 - step 1725: {'train/loss': 0.03151760995388031, 'train/grad_norm': 0.6067938208580017, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:08:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:08:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:33] train_sft.py:596 - step 1726: {'train/loss': 0.029541969299316406, 'train/grad_norm': 0.6759551763534546, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:08:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:08:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:38] train_sft.py:596 - step 1727: {'train/loss': 0.025888308882713318, 'train/grad_norm': 0.33910828828811646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:08:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:08:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:43] train_sft.py:596 - step 1728: {'train/loss': 0.02862367033958435, 'train/grad_norm': 0.6786302328109741, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 14:08:43] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:08:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:48] train_sft.py:596 - step 1729: {'train/loss': 0.031024277210235596, 'train/grad_norm': 0.5169404745101929, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:08:48] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:08:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:52] train_sft.py:596 - step 1730: {'train/loss': 0.03237342834472656, 'train/grad_norm': 0.7769873738288879, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:08:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:08:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:08:57] train_sft.py:596 - step 1731: {'train/loss': 0.02641206979751587, 'train/grad_norm': 0.43800175189971924, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:08:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:08:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:02] train_sft.py:596 - step 1732: {'train/loss': 0.03483915328979492, 'train/grad_norm': 0.9153515100479126, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 14:09:02] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:09:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:07] train_sft.py:596 - step 1733: {'train/loss': 0.024872153997421265, 'train/grad_norm': 0.5592120289802551, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:09:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:09:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:11] train_sft.py:596 - step 1734: {'train/loss': 0.03407701849937439, 'train/grad_norm': 0.8176506757736206, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:09:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:09:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:16] train_sft.py:596 - step 1735: {'train/loss': 0.03441053628921509, 'train/grad_norm': 0.720078706741333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:09:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:09:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:21] train_sft.py:596 - step 1736: {'train/loss': 0.030577003955841064, 'train/grad_norm': 0.5707874894142151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:09:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:09:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:25] train_sft.py:596 - step 1737: {'train/loss': 0.03334921598434448, 'train/grad_norm': 0.6064535975456238, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:09:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:09:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:30] train_sft.py:596 - step 1738: {'train/loss': 0.025658756494522095, 'train/grad_norm': 0.5688902139663696, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:09:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:09:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:35] train_sft.py:596 - step 1739: {'train/loss': 0.02258703112602234, 'train/grad_norm': 0.6772422194480896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:09:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:09:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:40] train_sft.py:596 - step 1740: {'train/loss': 0.02978992462158203, 'train/grad_norm': 0.6137620806694031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:09:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:09:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:44] train_sft.py:596 - step 1741: {'train/loss': 0.033190011978149414, 'train/grad_norm': 0.6872161030769348, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 14:09:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:09:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:49] train_sft.py:596 - step 1742: {'train/loss': 0.03322255611419678, 'train/grad_norm': 0.6041858196258545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:09:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:09:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:54] train_sft.py:596 - step 1743: {'train/loss': 0.021991923451423645, 'train/grad_norm': 0.4674297869205475, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:09:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:09:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:09:59] train_sft.py:596 - step 1744: {'train/loss': 0.03897738456726074, 'train/grad_norm': 0.6312505006790161, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:09:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:09:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:03] train_sft.py:596 - step 1745: {'train/loss': 0.027300357818603516, 'train/grad_norm': 0.41032129526138306, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:10:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:08] train_sft.py:596 - step 1746: {'train/loss': 0.03138101100921631, 'train/grad_norm': 0.4918898642063141, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:10:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:13] train_sft.py:596 - step 1747: {'train/loss': 0.020358100533485413, 'train/grad_norm': 0.48660457134246826, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:10:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:17] train_sft.py:596 - step 1748: {'train/loss': 0.02693915367126465, 'train/grad_norm': 0.5359948873519897, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:10:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:22] train_sft.py:596 - step 1749: {'train/loss': 0.02736620604991913, 'train/grad_norm': 0.5922224521636963, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 14:10:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:10:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:27] train_sft.py:596 - step 1750: {'train/loss': 0.02609483152627945, 'train/grad_norm': 0.5591649413108826, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:10:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:31] train_sft.py:596 - step 1751: {'train/loss': 0.03151094913482666, 'train/grad_norm': 0.5222628116607666, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:10:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:36] train_sft.py:596 - step 1752: {'train/loss': 0.029149293899536133, 'train/grad_norm': 0.6674798130989075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:10:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:41] train_sft.py:596 - step 1753: {'train/loss': 0.032658517360687256, 'train/grad_norm': 0.6459195613861084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:10:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:10:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:45] train_sft.py:596 - step 1754: {'train/loss': 0.038681745529174805, 'train/grad_norm': 0.9170190095901489, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:10:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:10:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:50] train_sft.py:596 - step 1755: {'train/loss': 0.03257942199707031, 'train/grad_norm': 0.7891579270362854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:10:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:55] train_sft.py:596 - step 1756: {'train/loss': 0.02489951252937317, 'train/grad_norm': 0.7633052468299866, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:10:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:10:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:10:59] train_sft.py:596 - step 1757: {'train/loss': 0.025522470474243164, 'train/grad_norm': 0.41868171095848083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:10:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:11:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:04] train_sft.py:596 - step 1758: {'train/loss': 0.03661081846803427, 'train/grad_norm': 0.6372398734092712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:11:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:11:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:09] train_sft.py:596 - step 1759: {'train/loss': 0.033933162689208984, 'train/grad_norm': 0.7230020761489868, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:11:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:11:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:14] train_sft.py:596 - step 1760: {'train/loss': 0.03210783004760742, 'train/grad_norm': 0.589218020439148, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:11:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:11:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:19] train_sft.py:596 - step 1761: {'train/loss': 0.02072322368621826, 'train/grad_norm': 0.3773612678050995, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:11:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:11:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:23] train_sft.py:596 - step 1762: {'train/loss': 0.030381180346012115, 'train/grad_norm': 0.5496437549591064, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:11:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:11:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:28] train_sft.py:596 - step 1763: {'train/loss': 0.028162717819213867, 'train/grad_norm': 0.5890207886695862, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:11:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:11:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:33] train_sft.py:596 - step 1764: {'train/loss': 0.027858614921569824, 'train/grad_norm': 0.5766617059707642, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 14:11:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:11:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:38] train_sft.py:596 - step 1765: {'train/loss': 0.03318595886230469, 'train/grad_norm': 0.6445521712303162, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:11:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:11:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:42] train_sft.py:596 - step 1766: {'train/loss': 0.0282914936542511, 'train/grad_norm': 0.5720623731613159, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:11:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:11:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:47] train_sft.py:596 - step 1767: {'train/loss': 0.02417675033211708, 'train/grad_norm': 0.5397205352783203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:11:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:11:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:51] train_sft.py:596 - step 1768: {'train/loss': 0.029337704181671143, 'train/grad_norm': 0.9596754312515259, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 14:11:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:11:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:11:56] train_sft.py:596 - step 1769: {'train/loss': 0.026043400168418884, 'train/grad_norm': 0.5324949622154236, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:11:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:11:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:01] train_sft.py:596 - step 1770: {'train/loss': 0.035894811153411865, 'train/grad_norm': 0.5603090524673462, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 14:12:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:12:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:05] train_sft.py:596 - step 1771: {'train/loss': 0.026596426963806152, 'train/grad_norm': 0.5570431351661682, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:12:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:10] train_sft.py:596 - step 1772: {'train/loss': 0.02744203805923462, 'train/grad_norm': 0.5821882486343384, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:12:10] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:12:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:15] train_sft.py:596 - step 1773: {'train/loss': 0.02687343955039978, 'train/grad_norm': 0.6828990578651428, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:12:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:20] train_sft.py:596 - step 1774: {'train/loss': 0.02323007583618164, 'train/grad_norm': 0.4818163812160492, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:12:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:24] train_sft.py:596 - step 1775: {'train/loss': 0.032781967893242836, 'train/grad_norm': 0.7335982322692871, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:12:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:12:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:29] train_sft.py:596 - step 1776: {'train/loss': 0.031832218170166016, 'train/grad_norm': 0.5876648426055908, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:12:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:34] train_sft.py:596 - step 1777: {'train/loss': 0.03443431854248047, 'train/grad_norm': 0.9049413800239563, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:12:34] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:12:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:38] train_sft.py:596 - step 1778: {'train/loss': 0.031524062156677246, 'train/grad_norm': 0.40990179777145386, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:12:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:43] train_sft.py:596 - step 1779: {'train/loss': 0.04702877998352051, 'train/grad_norm': 0.7056434750556946, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:12:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:47] train_sft.py:596 - step 1780: {'train/loss': 0.031957387924194336, 'train/grad_norm': 0.5097586512565613, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:12:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:12:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:52] train_sft.py:596 - step 1781: {'train/loss': 0.04237467050552368, 'train/grad_norm': 0.7267534732818604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:12:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:12:57] train_sft.py:596 - step 1782: {'train/loss': 0.03692364692687988, 'train/grad_norm': 0.48208194971084595, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:12:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:12:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:02] train_sft.py:596 - step 1783: {'train/loss': 0.041367918252944946, 'train/grad_norm': 0.4991135895252228, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:13:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:13:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:06] train_sft.py:596 - step 1784: {'train/loss': 0.02429330348968506, 'train/grad_norm': 0.48732835054397583, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:13:06] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:13:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:11] train_sft.py:596 - step 1785: {'train/loss': 0.029345810413360596, 'train/grad_norm': 0.5967838764190674, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:13:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:13:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:16] train_sft.py:596 - step 1786: {'train/loss': 0.03350567817687988, 'train/grad_norm': 0.7596781849861145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:13:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:13:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:21] train_sft.py:596 - step 1787: {'train/loss': 0.04012376070022583, 'train/grad_norm': 0.7660601139068604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:13:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:13:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:25] train_sft.py:596 - step 1788: {'train/loss': 0.03131798654794693, 'train/grad_norm': 0.6626997590065002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 14:13:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:13:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:30] train_sft.py:596 - step 1789: {'train/loss': 0.03120243549346924, 'train/grad_norm': 0.5058810710906982, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:13:30] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:13:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:35] train_sft.py:596 - step 1790: {'train/loss': 0.03696870803833008, 'train/grad_norm': 0.5655524134635925, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:13:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:13:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:40] train_sft.py:596 - step 1791: {'train/loss': 0.02680826187133789, 'train/grad_norm': 0.6198005080223083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:13:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:13:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:44] train_sft.py:596 - step 1792: {'train/loss': 0.03223586082458496, 'train/grad_norm': 0.5148496627807617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:13:44] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:13:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:49] train_sft.py:596 - step 1793: {'train/loss': 0.04150503873825073, 'train/grad_norm': 0.8799224495887756, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:13:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:13:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:54] train_sft.py:596 - step 1794: {'train/loss': 0.028152763843536377, 'train/grad_norm': 0.6025766134262085, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 14:13:54] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:13:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:13:59] train_sft.py:596 - step 1795: {'train/loss': 0.030172288417816162, 'train/grad_norm': 0.6341298818588257, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 14:13:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:13:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:03] train_sft.py:596 - step 1796: {'train/loss': 0.02323845773935318, 'train/grad_norm': 0.4170074462890625, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:14:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:08] train_sft.py:596 - step 1797: {'train/loss': 0.03938207030296326, 'train/grad_norm': 0.8041067123413086, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:14:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:13] train_sft.py:596 - step 1798: {'train/loss': 0.04219174385070801, 'train/grad_norm': 0.824054479598999, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:14:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:14:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:18] train_sft.py:596 - step 1799: {'train/loss': 0.031182080507278442, 'train/grad_norm': 0.5391349196434021, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:14:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:22] train_sft.py:596 - step 1800: {'train/loss': 0.033380910754203796, 'train/grad_norm': 0.5813719034194946, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:14:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:27] train_sft.py:596 - step 1801: {'train/loss': 0.028297126293182373, 'train/grad_norm': 0.6587638854980469, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:14:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:31] train_sft.py:596 - step 1802: {'train/loss': 0.0180966854095459, 'train/grad_norm': 0.5859122276306152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:14:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:36] train_sft.py:596 - step 1803: {'train/loss': 0.018465876579284668, 'train/grad_norm': 0.4346745014190674, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:14:36] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:14:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:41] train_sft.py:596 - step 1804: {'train/loss': 0.02140647917985916, 'train/grad_norm': 0.45354923605918884, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 14:14:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:14:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:46] train_sft.py:596 - step 1805: {'train/loss': 0.0201416015625, 'train/grad_norm': 0.4210176467895508, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:14:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:14:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:51] train_sft.py:596 - step 1806: {'train/loss': 0.01950782537460327, 'train/grad_norm': 0.48400789499282837, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:14:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:14:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:14:56] train_sft.py:596 - step 1807: {'train/loss': 0.024053096771240234, 'train/grad_norm': 0.37331393361091614, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 14:14:56] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:14:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:00] train_sft.py:596 - step 1808: {'train/loss': 0.02321441099047661, 'train/grad_norm': 0.44152823090553284, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:15:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:15:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:06] train_sft.py:596 - step 1809: {'train/loss': 0.0174724068492651, 'train/grad_norm': 0.24623055756092072, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 14:15:06] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:15:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:10] train_sft.py:596 - step 1810: {'train/loss': 0.021575096994638443, 'train/grad_norm': 0.8029443025588989, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:15:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:15] train_sft.py:596 - step 1811: {'train/loss': 0.0262300968170166, 'train/grad_norm': 0.6770822405815125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 14:15:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:15:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:20] train_sft.py:596 - step 1812: {'train/loss': 0.020227432250976562, 'train/grad_norm': 0.4441028833389282, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:15:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:25] train_sft.py:596 - step 1813: {'train/loss': 0.03231417387723923, 'train/grad_norm': 0.860233724117279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:15:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:30] train_sft.py:596 - step 1814: {'train/loss': 0.024045944213867188, 'train/grad_norm': 0.6307662129402161, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:15:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:15:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:34] train_sft.py:596 - step 1815: {'train/loss': 0.02746478095650673, 'train/grad_norm': 0.73997563123703, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:15:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:39] train_sft.py:596 - step 1816: {'train/loss': 0.02065715193748474, 'train/grad_norm': 0.5017306804656982, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:15:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:44] train_sft.py:596 - step 1817: {'train/loss': 0.02641192078590393, 'train/grad_norm': 0.835099995136261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:15:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:48] train_sft.py:596 - step 1818: {'train/loss': 0.02317604422569275, 'train/grad_norm': 0.5666943192481995, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:15:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:53] train_sft.py:596 - step 1819: {'train/loss': 0.015558302402496338, 'train/grad_norm': 0.4218175411224365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:15:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:15:58] train_sft.py:596 - step 1820: {'train/loss': 0.017847582697868347, 'train/grad_norm': 0.5190432667732239, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:15:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:15:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:02] train_sft.py:596 - step 1821: {'train/loss': 0.01489819586277008, 'train/grad_norm': 0.5829293131828308, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:16:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:16:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:07] train_sft.py:596 - step 1822: {'train/loss': 0.019426822662353516, 'train/grad_norm': 0.433439701795578, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 14:16:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:16:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:12] train_sft.py:596 - step 1823: {'train/loss': 0.021062642335891724, 'train/grad_norm': 0.7710361480712891, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:16:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:16:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:17] train_sft.py:596 - step 1824: {'train/loss': 0.015314340591430664, 'train/grad_norm': 0.3797142803668976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:16:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:16:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:22] train_sft.py:596 - step 1825: {'train/loss': 0.01919613778591156, 'train/grad_norm': 0.7533341646194458, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:16:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:16:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:26] train_sft.py:596 - step 1826: {'train/loss': 0.029562175273895264, 'train/grad_norm': 0.6669799089431763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:16:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:16:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:31] train_sft.py:596 - step 1827: {'train/loss': 0.01997506618499756, 'train/grad_norm': 0.4121215343475342, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:16:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:16:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:36] train_sft.py:596 - step 1828: {'train/loss': 0.022114026360213757, 'train/grad_norm': 0.8328877091407776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:16:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:16:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:41] train_sft.py:596 - step 1829: {'train/loss': 0.023400813341140747, 'train/grad_norm': 0.3000933825969696, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.12s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.12s/it]
[2025-12-29 14:16:41] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 14:16:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:46] train_sft.py:596 - step 1830: {'train/loss': 0.02676108479499817, 'train/grad_norm': 0.4528714120388031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 14:16:46] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:16:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:51] train_sft.py:596 - step 1831: {'train/loss': 0.03495876491069794, 'train/grad_norm': 0.6298444271087646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:16:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:16:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:16:56] train_sft.py:596 - step 1832: {'train/loss': 0.02014082670211792, 'train/grad_norm': 0.39015012979507446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:16:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:16:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:00] train_sft.py:596 - step 1833: {'train/loss': 0.0254591703414917, 'train/grad_norm': 0.7661634087562561, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:17:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:17:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:05] train_sft.py:596 - step 1834: {'train/loss': 0.021483300253748894, 'train/grad_norm': 1.6277729272842407, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:17:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:17:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:10] train_sft.py:596 - step 1835: {'train/loss': 0.018887817859649658, 'train/grad_norm': 0.5280170440673828, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:17:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:17:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:14] train_sft.py:596 - step 1836: {'train/loss': 0.025141209363937378, 'train/grad_norm': 0.6358638405799866, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:17:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:17:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:19] train_sft.py:596 - step 1837: {'train/loss': 0.01737654209136963, 'train/grad_norm': 0.6291924715042114, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:17:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:17:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:24] train_sft.py:596 - step 1838: {'train/loss': 0.02274777740240097, 'train/grad_norm': 0.6014887094497681, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:17:24] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:17:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:29] train_sft.py:596 - step 1839: {'train/loss': 0.025596335530281067, 'train/grad_norm': 0.5380093455314636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:17:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:17:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:34] train_sft.py:596 - step 1840: {'train/loss': 0.021119050681591034, 'train/grad_norm': 0.5252216458320618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:17:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:17:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:39] train_sft.py:596 - step 1841: {'train/loss': 0.024132370948791504, 'train/grad_norm': 0.4265250861644745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 14:17:39] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:17:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:44] train_sft.py:596 - step 1842: {'train/loss': 0.021593332290649414, 'train/grad_norm': 0.4886215925216675, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:17:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:17:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:49] train_sft.py:596 - step 1843: {'train/loss': 0.027341485023498535, 'train/grad_norm': 0.8667563796043396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:17:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:17:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:54] train_sft.py:596 - step 1844: {'train/loss': 0.03649425506591797, 'train/grad_norm': 0.6716073155403137, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:17:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:17:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:17:58] train_sft.py:596 - step 1845: {'train/loss': 0.026044756174087524, 'train/grad_norm': 0.5448119044303894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:17:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:17:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:03] train_sft.py:596 - step 1846: {'train/loss': 0.02208438515663147, 'train/grad_norm': 0.5456441044807434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:18:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:18:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:08] train_sft.py:596 - step 1847: {'train/loss': 0.018086671829223633, 'train/grad_norm': 0.34823179244995117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 14:18:08] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:18:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:13] train_sft.py:596 - step 1848: {'train/loss': 0.016306385397911072, 'train/grad_norm': 0.3672437071800232, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:18:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:18:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:18] train_sft.py:596 - step 1849: {'train/loss': 0.018743515014648438, 'train/grad_norm': 0.33086884021759033, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:18:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:18:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:23] train_sft.py:596 - step 1850: {'train/loss': 0.016985520720481873, 'train/grad_norm': 0.30534830689430237, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 14:18:23] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:18:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:28] train_sft.py:596 - step 1851: {'train/loss': 0.031972140073776245, 'train/grad_norm': 0.503026008605957, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:18:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:18:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:32] train_sft.py:596 - step 1852: {'train/loss': 0.02775171399116516, 'train/grad_norm': 0.7252698540687561, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:18:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:18:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:38] train_sft.py:596 - step 1853: {'train/loss': 0.017241209745407104, 'train/grad_norm': 0.5956338047981262, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 14:18:38] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:18:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:43] train_sft.py:596 - step 1854: {'train/loss': 0.01937258243560791, 'train/grad_norm': 0.3793157935142517, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:18:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:18:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:47] train_sft.py:596 - step 1855: {'train/loss': 0.021270334720611572, 'train/grad_norm': 0.4363947808742523, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 14:18:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:18:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:53] train_sft.py:596 - step 1856: {'train/loss': 0.023083478212356567, 'train/grad_norm': 0.40550893545150757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:18:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:18:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:18:58] train_sft.py:596 - step 1857: {'train/loss': 0.0260714590549469, 'train/grad_norm': 0.6708617806434631, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:18:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:18:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:02] train_sft.py:596 - step 1858: {'train/loss': 0.05813285708427429, 'train/grad_norm': 0.7206395268440247, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:19:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:19:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:07] train_sft.py:596 - step 1859: {'train/loss': 0.02455104887485504, 'train/grad_norm': 0.6222780346870422, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:19:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:19:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:12] train_sft.py:596 - step 1860: {'train/loss': 0.02317415177822113, 'train/grad_norm': 0.608586847782135, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:19:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:19:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:17] train_sft.py:596 - step 1861: {'train/loss': 0.022545471787452698, 'train/grad_norm': 0.436755895614624, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:19:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:19:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:22] train_sft.py:596 - step 1862: {'train/loss': 0.03236708045005798, 'train/grad_norm': 0.8973721861839294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:19:22] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:19:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:27] train_sft.py:596 - step 1863: {'train/loss': 0.025627732276916504, 'train/grad_norm': 0.7912241816520691, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 14:19:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:19:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:31] train_sft.py:596 - step 1864: {'train/loss': 0.01940903067588806, 'train/grad_norm': 0.46407580375671387, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:19:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:19:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:36] train_sft.py:596 - step 1865: {'train/loss': 0.013335436582565308, 'train/grad_norm': 0.3425946533679962, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:19:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:19:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:41] train_sft.py:596 - step 1866: {'train/loss': 0.01747526228427887, 'train/grad_norm': 0.3893781006336212, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 14:19:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:19:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:46] train_sft.py:596 - step 1867: {'train/loss': 0.01656229794025421, 'train/grad_norm': 0.6946840882301331, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 14:19:46] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:19:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:51] train_sft.py:596 - step 1868: {'train/loss': 0.023234426975250244, 'train/grad_norm': 0.9489771723747253, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 14:19:51] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:19:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:19:56] train_sft.py:596 - step 1869: {'train/loss': 0.023161470890045166, 'train/grad_norm': 0.5956576466560364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:19:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:19:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:01] train_sft.py:596 - step 1870: {'train/loss': 0.026174157857894897, 'train/grad_norm': 0.805046021938324, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 14:20:01] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:20:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:06] train_sft.py:596 - step 1871: {'train/loss': 0.029455482959747314, 'train/grad_norm': 0.6604488492012024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:20:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:20:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:11] train_sft.py:596 - step 1872: {'train/loss': 0.02339261770248413, 'train/grad_norm': 0.3886626958847046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 14:20:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:20:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:16] train_sft.py:596 - step 1873: {'train/loss': 0.021170496940612793, 'train/grad_norm': 0.506801426410675, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 14:20:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:20:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:21] train_sft.py:596 - step 1874: {'train/loss': 0.024395860731601715, 'train/grad_norm': 0.6343125104904175, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 14:20:21] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:20:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:25] train_sft.py:596 - step 1875: {'train/loss': 0.02073812484741211, 'train/grad_norm': 0.5294897556304932, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:20:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:20:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:30] train_sft.py:596 - step 1876: {'train/loss': 0.023946158587932587, 'train/grad_norm': 0.44882339239120483, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:20:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:20:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:35] train_sft.py:596 - step 1877: {'train/loss': 0.029893696308135986, 'train/grad_norm': 0.8981168866157532, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:20:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:20:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:40] train_sft.py:596 - step 1878: {'train/loss': 0.015717778354883194, 'train/grad_norm': 0.3460301160812378, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:20:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:20:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:45] train_sft.py:596 - step 1879: {'train/loss': 0.02639526128768921, 'train/grad_norm': 0.38173019886016846, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:20:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:20:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:50] train_sft.py:596 - step 1880: {'train/loss': 0.021113693714141846, 'train/grad_norm': 0.5325926542282104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:20:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:20:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:54] train_sft.py:596 - step 1881: {'train/loss': 0.02785971760749817, 'train/grad_norm': 0.6473944187164307, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:20:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:20:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:20:59] train_sft.py:596 - step 1882: {'train/loss': 0.02872180938720703, 'train/grad_norm': 0.724972128868103, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:20:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:20:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:04] train_sft.py:596 - step 1883: {'train/loss': 0.022157609462738037, 'train/grad_norm': 0.4768298268318176, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 14:21:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:21:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:09] train_sft.py:596 - step 1884: {'train/loss': 0.02204906940460205, 'train/grad_norm': 0.4969187080860138, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:21:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:21:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:14] train_sft.py:596 - step 1885: {'train/loss': 0.03329825401306152, 'train/grad_norm': 0.9038977026939392, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:21:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:21:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:19] train_sft.py:596 - step 1886: {'train/loss': 0.023519039154052734, 'train/grad_norm': 0.3958936631679535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 14:21:19] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:21:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:24] train_sft.py:596 - step 1887: {'train/loss': 0.028033018112182617, 'train/grad_norm': 0.6363586783409119, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:21:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:21:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:29] train_sft.py:596 - step 1888: {'train/loss': 0.015956435352563858, 'train/grad_norm': 0.5692132115364075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:21:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:21:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:33] train_sft.py:596 - step 1889: {'train/loss': 0.03335164487361908, 'train/grad_norm': 0.5563547015190125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 14:21:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:21:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:38] train_sft.py:596 - step 1890: {'train/loss': 0.0213758647441864, 'train/grad_norm': 0.38417917490005493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:21:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:21:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:43] train_sft.py:596 - step 1891: {'train/loss': 0.02484200894832611, 'train/grad_norm': 0.4582645893096924, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:21:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:21:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:48] train_sft.py:596 - step 1892: {'train/loss': 0.015227727591991425, 'train/grad_norm': 0.47906994819641113, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:21:48] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:21:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:53] train_sft.py:596 - step 1893: {'train/loss': 0.021044090390205383, 'train/grad_norm': 0.5245971083641052, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:21:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:21:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:21:57] train_sft.py:596 - step 1894: {'train/loss': 0.026321426033973694, 'train/grad_norm': 0.7567977905273438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:21:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:21:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:02] train_sft.py:596 - step 1895: {'train/loss': 0.023504123091697693, 'train/grad_norm': 0.6495212316513062, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:22:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:22:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:07] train_sft.py:596 - step 1896: {'train/loss': 0.022448420524597168, 'train/grad_norm': 0.8066319227218628, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:22:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:22:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:11] train_sft.py:596 - step 1897: {'train/loss': 0.019174665212631226, 'train/grad_norm': 0.4596607983112335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:22:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:22:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:16] train_sft.py:596 - step 1898: {'train/loss': 0.024727702140808105, 'train/grad_norm': 0.5510536432266235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:22:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:22:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:21] train_sft.py:596 - step 1899: {'train/loss': 0.023904014378786087, 'train/grad_norm': 0.6339291930198669, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 14:22:21] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:22:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:26] train_sft.py:596 - step 1900: {'train/loss': 0.024486780166625977, 'train/grad_norm': 0.8218720555305481, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:22:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:22:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:31] train_sft.py:596 - step 1901: {'train/loss': 0.021550685167312622, 'train/grad_norm': 0.3982655704021454, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:22:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:22:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:36] train_sft.py:596 - step 1902: {'train/loss': 0.024669289588928223, 'train/grad_norm': 0.43008047342300415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:22:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:22:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:41] train_sft.py:596 - step 1903: {'train/loss': 0.034117937088012695, 'train/grad_norm': 0.7318738102912903, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:22:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:22:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:45] train_sft.py:596 - step 1904: {'train/loss': 0.02711343765258789, 'train/grad_norm': 0.5121325850486755, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:22:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:22:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:50] train_sft.py:596 - step 1905: {'train/loss': 0.02657756209373474, 'train/grad_norm': 0.5127648711204529, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:22:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:22:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:22:55] train_sft.py:596 - step 1906: {'train/loss': 0.02375391125679016, 'train/grad_norm': 0.6243616342544556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:22:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:22:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:00] train_sft.py:596 - step 1907: {'train/loss': 0.01836267113685608, 'train/grad_norm': 0.5259712934494019, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:23:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:23:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:04] train_sft.py:596 - step 1908: {'train/loss': 0.021170973777770996, 'train/grad_norm': 0.6397424936294556, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:23:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:23:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:09] train_sft.py:596 - step 1909: {'train/loss': 0.019509024918079376, 'train/grad_norm': 0.35803648829460144, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:23:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:23:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:14] train_sft.py:596 - step 1910: {'train/loss': 0.028085291385650635, 'train/grad_norm': 0.49008551239967346, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 14:23:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:23:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:19] train_sft.py:596 - step 1911: {'train/loss': 0.027386009693145752, 'train/grad_norm': 0.5816437602043152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 14:23:19] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:23:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:24] train_sft.py:596 - step 1912: {'train/loss': 0.02959918975830078, 'train/grad_norm': 0.7456693649291992, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:23:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:23:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:29] train_sft.py:596 - step 1913: {'train/loss': 0.025552421808242798, 'train/grad_norm': 0.5906476974487305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:23:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:23:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:34] train_sft.py:596 - step 1914: {'train/loss': 0.03475767374038696, 'train/grad_norm': 0.7392396926879883, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:23:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:23:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:38] train_sft.py:596 - step 1915: {'train/loss': 0.025438793003559113, 'train/grad_norm': 0.3946228325366974, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:23:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:23:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:43] train_sft.py:596 - step 1916: {'train/loss': 0.02165353298187256, 'train/grad_norm': 0.4024849832057953, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:23:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:23:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:48] train_sft.py:596 - step 1917: {'train/loss': 0.01928102970123291, 'train/grad_norm': 0.3807535171508789, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:23:48] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:23:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:53] train_sft.py:596 - step 1918: {'train/loss': 0.0313328392803669, 'train/grad_norm': 0.6988074779510498, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:23:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:23:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:23:57] train_sft.py:596 - step 1919: {'train/loss': 0.024596482515335083, 'train/grad_norm': 0.741445779800415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:23:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:23:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:03] train_sft.py:596 - step 1920: {'train/loss': 0.01718384027481079, 'train/grad_norm': 0.3666516840457916, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 14:24:03] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:24:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:07] train_sft.py:596 - step 1921: {'train/loss': 0.024392619729042053, 'train/grad_norm': 0.6053125858306885, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:24:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:24:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:12] train_sft.py:596 - step 1922: {'train/loss': 0.02483387291431427, 'train/grad_norm': 0.53965163230896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:24:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:24:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:17] train_sft.py:596 - step 1923: {'train/loss': 0.031136855483055115, 'train/grad_norm': 0.5080841779708862, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:24:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:24:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:22] train_sft.py:596 - step 1924: {'train/loss': 0.024174004793167114, 'train/grad_norm': 0.8154908418655396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:24:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:24:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:26] train_sft.py:596 - step 1925: {'train/loss': 0.01725947856903076, 'train/grad_norm': 0.42551857233047485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:24:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:24:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:31] train_sft.py:596 - step 1926: {'train/loss': 0.02564406394958496, 'train/grad_norm': 0.48770302534103394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:24:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:24:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:36] train_sft.py:596 - step 1927: {'train/loss': 0.024311020970344543, 'train/grad_norm': 0.3675059378147125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:24:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:24:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:40] train_sft.py:596 - step 1928: {'train/loss': 0.02036839723587036, 'train/grad_norm': 0.5337696075439453, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:24:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:24:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:45] train_sft.py:596 - step 1929: {'train/loss': 0.02563410997390747, 'train/grad_norm': 0.4726699888706207, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:24:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:24:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:50] train_sft.py:596 - step 1930: {'train/loss': 0.030419349670410156, 'train/grad_norm': 0.9274410605430603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:24:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:24:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:54] train_sft.py:596 - step 1931: {'train/loss': 0.023131966590881348, 'train/grad_norm': 0.7506514191627502, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:24:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:24:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:24:59] train_sft.py:596 - step 1932: {'train/loss': 0.021974503993988037, 'train/grad_norm': 0.816232442855835, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:24:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:24:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:04] train_sft.py:596 - step 1933: {'train/loss': 0.028666019439697266, 'train/grad_norm': 0.757901132106781, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:25:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:25:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:08] train_sft.py:596 - step 1934: {'train/loss': 0.02420288324356079, 'train/grad_norm': 0.8216579556465149, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:25:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:25:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:13] train_sft.py:596 - step 1935: {'train/loss': 0.022265195846557617, 'train/grad_norm': 0.68105548620224, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 14:25:13] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:25:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:18] train_sft.py:596 - step 1936: {'train/loss': 0.025523394346237183, 'train/grad_norm': 0.5400129556655884, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:25:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:25:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:23] train_sft.py:596 - step 1937: {'train/loss': 0.026736170053482056, 'train/grad_norm': 0.4316241145133972, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:25:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:25:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:28] train_sft.py:596 - step 1938: {'train/loss': 0.022266551852226257, 'train/grad_norm': 0.5529821515083313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:25:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:25:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:32] train_sft.py:596 - step 1939: {'train/loss': 0.018545687198638916, 'train/grad_norm': 0.43800222873687744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:25:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:25:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:37] train_sft.py:596 - step 1940: {'train/loss': 0.02503359317779541, 'train/grad_norm': 0.5193010568618774, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:25:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:25:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:42] train_sft.py:596 - step 1941: {'train/loss': 0.025385726243257523, 'train/grad_norm': 0.5160126686096191, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:25:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:25:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:46] train_sft.py:596 - step 1942: {'train/loss': 0.0249977707862854, 'train/grad_norm': 0.68067467212677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:25:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:25:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:51] train_sft.py:596 - step 1943: {'train/loss': 0.015184611082077026, 'train/grad_norm': 0.45091623067855835, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:25:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:25:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:25:56] train_sft.py:596 - step 1944: {'train/loss': 0.027282193303108215, 'train/grad_norm': 0.5940497517585754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:25:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:25:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:01] train_sft.py:596 - step 1945: {'train/loss': 0.026892483234405518, 'train/grad_norm': 0.49506205320358276, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:26:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:26:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:05] train_sft.py:596 - step 1946: {'train/loss': 0.0296628475189209, 'train/grad_norm': 0.46785232424736023, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:26:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:26:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:10] train_sft.py:596 - step 1947: {'train/loss': 0.03203099966049194, 'train/grad_norm': 0.5812214016914368, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:26:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:26:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:15] train_sft.py:596 - step 1948: {'train/loss': 0.02231431007385254, 'train/grad_norm': 0.4621424078941345, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:26:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:26:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:19] train_sft.py:596 - step 1949: {'train/loss': 0.03585028648376465, 'train/grad_norm': 0.6357353925704956, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:26:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:26:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:24] train_sft.py:596 - step 1950: {'train/loss': 0.021393567323684692, 'train/grad_norm': 0.5110552310943604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:26:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:26:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:29] train_sft.py:596 - step 1951: {'train/loss': 0.0282670259475708, 'train/grad_norm': 0.7728589773178101, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:26:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:26:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:33] train_sft.py:596 - step 1952: {'train/loss': 0.031014442443847656, 'train/grad_norm': 0.4860377013683319, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:26:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:26:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:38] train_sft.py:596 - step 1953: {'train/loss': 0.026504792273044586, 'train/grad_norm': 0.6821836233139038, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:26:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:26:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:42] train_sft.py:596 - step 1954: {'train/loss': 0.019630033522844315, 'train/grad_norm': 0.40395042300224304, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:26:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:26:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:47] train_sft.py:596 - step 1955: {'train/loss': 0.03238940238952637, 'train/grad_norm': 0.6750469207763672, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:26:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:26:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:52] train_sft.py:596 - step 1956: {'train/loss': 0.029460027813911438, 'train/grad_norm': 0.8934852480888367, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:26:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:26:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:26:56] train_sft.py:596 - step 1957: {'train/loss': 0.03786170482635498, 'train/grad_norm': 0.8376238346099854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:26:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:26:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:01] train_sft.py:596 - step 1958: {'train/loss': 0.02758762240409851, 'train/grad_norm': 0.847623884677887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:27:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:06] train_sft.py:596 - step 1959: {'train/loss': 0.025881052017211914, 'train/grad_norm': 0.4939942955970764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:27:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:27:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:10] train_sft.py:596 - step 1960: {'train/loss': 0.030556008219718933, 'train/grad_norm': 0.7949535250663757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:27:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:27:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:15] train_sft.py:596 - step 1961: {'train/loss': 0.0271756649017334, 'train/grad_norm': 1.0032774209976196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:27:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:20] train_sft.py:596 - step 1962: {'train/loss': 0.02892954647541046, 'train/grad_norm': 1.056958794593811, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:27:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:24] train_sft.py:596 - step 1963: {'train/loss': 0.02793341875076294, 'train/grad_norm': 0.8986667990684509, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:27:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:29] train_sft.py:596 - step 1964: {'train/loss': 0.031839847564697266, 'train/grad_norm': 0.8844723105430603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:27:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:27:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:33] train_sft.py:596 - step 1965: {'train/loss': 0.026169002056121826, 'train/grad_norm': 0.9009175896644592, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:27:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:38] train_sft.py:596 - step 1966: {'train/loss': 0.026558637619018555, 'train/grad_norm': 0.5843122005462646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:27:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:27:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:43] train_sft.py:596 - step 1967: {'train/loss': 0.026377305388450623, 'train/grad_norm': 0.5637481212615967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:27:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:47] train_sft.py:596 - step 1968: {'train/loss': 0.02681061625480652, 'train/grad_norm': 0.45604804158210754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:27:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:27:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:52] train_sft.py:596 - step 1969: {'train/loss': 0.02006646990776062, 'train/grad_norm': 0.6240450739860535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:27:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:27:57] train_sft.py:596 - step 1970: {'train/loss': 0.02866586297750473, 'train/grad_norm': 0.4281467795372009, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:27:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:27:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:02] train_sft.py:596 - step 1971: {'train/loss': 0.040728986263275146, 'train/grad_norm': 0.6342520117759705, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:28:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:28:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:07] train_sft.py:596 - step 1972: {'train/loss': 0.0309445858001709, 'train/grad_norm': 0.7674857378005981, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:28:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:28:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:11] train_sft.py:596 - step 1973: {'train/loss': 0.026505470275878906, 'train/grad_norm': 0.5422592163085938, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:28:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:28:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:16] train_sft.py:596 - step 1974: {'train/loss': 0.024499312043190002, 'train/grad_norm': 0.5733706951141357, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:28:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:28:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:21] train_sft.py:596 - step 1975: {'train/loss': 0.029869675636291504, 'train/grad_norm': 0.4807339906692505, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:28:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:28:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:26] train_sft.py:596 - step 1976: {'train/loss': 0.022252559661865234, 'train/grad_norm': 0.5935763716697693, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:28:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:28:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:30] train_sft.py:596 - step 1977: {'train/loss': 0.035843849182128906, 'train/grad_norm': 1.190375566482544, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:28:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:28:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:35] train_sft.py:596 - step 1978: {'train/loss': 0.03302335739135742, 'train/grad_norm': 0.5000287294387817, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:28:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:28:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:40] train_sft.py:596 - step 1979: {'train/loss': 0.031641602516174316, 'train/grad_norm': 0.4621354341506958, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:28:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:28:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:45] train_sft.py:596 - step 1980: {'train/loss': 0.019612222909927368, 'train/grad_norm': 0.510276198387146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
[2025-12-29 14:28:45] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:28:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:50] train_sft.py:596 - step 1981: {'train/loss': 0.020860813558101654, 'train/grad_norm': 0.5052135586738586, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:28:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:28:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:55] train_sft.py:596 - step 1982: {'train/loss': 0.023066222667694092, 'train/grad_norm': 0.4398925006389618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:28:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:28:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:28:59] train_sft.py:596 - step 1983: {'train/loss': 0.020695865154266357, 'train/grad_norm': 0.5768865942955017, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:28:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:28:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:04] train_sft.py:596 - step 1984: {'train/loss': 0.02413181960582733, 'train/grad_norm': 0.4510345757007599, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:29:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:29:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:09] train_sft.py:596 - step 1985: {'train/loss': 0.038829416036605835, 'train/grad_norm': 0.6914569735527039, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:29:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:13] train_sft.py:596 - step 1986: {'train/loss': 0.027015358209609985, 'train/grad_norm': 0.4459548592567444, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:29:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:29:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:18] train_sft.py:596 - step 1987: {'train/loss': 0.024591535329818726, 'train/grad_norm': 0.5549681186676025, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:29:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:23] train_sft.py:596 - step 1988: {'train/loss': 0.022000111639499664, 'train/grad_norm': 0.6514590978622437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:29:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:27] train_sft.py:596 - step 1989: {'train/loss': 0.028492331504821777, 'train/grad_norm': 0.8240971565246582, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 14:29:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:32] train_sft.py:596 - step 1990: {'train/loss': 0.026577770709991455, 'train/grad_norm': 0.6673269271850586, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:29:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:29:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:37] train_sft.py:596 - step 1991: {'train/loss': 0.024095825850963593, 'train/grad_norm': 0.45923206210136414, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:29:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:41] train_sft.py:596 - step 1992: {'train/loss': 0.01994454860687256, 'train/grad_norm': 0.4896993339061737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:29:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:29:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:46] train_sft.py:596 - step 1993: {'train/loss': 0.02280651032924652, 'train/grad_norm': 0.5591368079185486, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:29:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:51] train_sft.py:596 - step 1994: {'train/loss': 0.022163838148117065, 'train/grad_norm': 0.5572700500488281, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 14:29:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:29:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:29:56] train_sft.py:596 - step 1995: {'train/loss': 0.02675338089466095, 'train/grad_norm': 0.5819255709648132, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:29:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:29:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:30:00] train_sft.py:596 - step 1996: {'train/loss': 0.0323091521859169, 'train/grad_norm': 0.7111276388168335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:30:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:30:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:30:05] train_sft.py:596 - step 1997: {'train/loss': 0.02124476432800293, 'train/grad_norm': 0.4728248715400696, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:30:05] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:30:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:30:10] train_sft.py:596 - step 1998: {'train/loss': 0.0289953351020813, 'train/grad_norm': 0.9002489447593689, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:30:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:30:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:30:15] train_sft.py:596 - step 1999: {'train/loss': 0.023492813110351562, 'train/grad_norm': 0.528927743434906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:30:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:30:27] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0002000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:03<05:20,  3.24s/it]
actor_val:   2%|▏         | 2/100 [00:04<03:43,  2.28s/it]
actor_val:   3%|▎         | 3/100 [00:06<03:10,  1.96s/it]
actor_val:   4%|▍         | 4/100 [00:08<02:53,  1.81s/it]
actor_val:   5%|▌         | 5/100 [00:10<03:00,  1.90s/it]
actor_val:   6%|▌         | 6/100 [00:11<02:50,  1.81s/it]
actor_val:   7%|▋         | 7/100 [00:13<02:50,  1.84s/it]
actor_val:   8%|▊         | 8/100 [00:15<02:39,  1.74s/it]
actor_val:   9%|▉         | 9/100 [00:16<02:40,  1.77s/it]
actor_val:  10%|█         | 10/100 [00:18<02:39,  1.77s/it]
actor_val:  11%|█         | 11/100 [00:20<02:40,  1.80s/it]
actor_val:  12%|█▏        | 12/100 [00:22<02:31,  1.72s/it]
actor_val:  13%|█▎        | 13/100 [00:24<02:33,  1.77s/it]
actor_val:  14%|█▍        | 14/100 [00:25<02:32,  1.77s/it]
actor_val:  15%|█▌        | 15/100 [00:27<02:32,  1.80s/it]
actor_val:  16%|█▌        | 16/100 [00:29<02:24,  1.72s/it]
actor_val:  17%|█▋        | 17/100 [00:31<02:25,  1.75s/it]
actor_val:  18%|█▊        | 18/100 [00:32<02:25,  1.77s/it]
actor_val:  19%|█▉        | 19/100 [00:34<02:24,  1.78s/it]
actor_val:  20%|██        | 20/100 [00:36<02:16,  1.71s/it]
actor_val:  21%|██        | 21/100 [00:37<02:11,  1.67s/it]
actor_val:  22%|██▏       | 22/100 [00:39<02:15,  1.74s/it]
actor_val:  23%|██▎       | 23/100 [00:41<02:10,  1.70s/it]
actor_val:  24%|██▍       | 24/100 [00:43<02:12,  1.74s/it]
actor_val:  25%|██▌       | 25/100 [00:44<02:07,  1.69s/it]
actor_val:  26%|██▌       | 26/100 [00:46<02:08,  1.74s/it]
actor_val:  27%|██▋       | 27/100 [00:48<02:07,  1.75s/it]
actor_val:  28%|██▊       | 28/100 [00:49<02:01,  1.69s/it]
actor_val:  29%|██▉       | 29/100 [00:51<02:03,  1.75s/it]
actor_val:  30%|███       | 30/100 [00:53<02:04,  1.78s/it]
actor_val:  31%|███       | 31/100 [00:55<01:58,  1.71s/it]
actor_val:  32%|███▏      | 32/100 [00:56<01:53,  1.68s/it]
actor_val:  33%|███▎      | 33/100 [00:58<01:55,  1.72s/it]
actor_val:  34%|███▍      | 34/100 [01:00<01:50,  1.67s/it]
actor_val:  35%|███▌      | 35/100 [01:01<01:47,  1.65s/it]
actor_val:  36%|███▌      | 36/100 [01:03<01:44,  1.63s/it]
actor_val:  37%|███▋      | 37/100 [01:04<01:41,  1.62s/it]
actor_val:  38%|███▊      | 38/100 [01:06<01:38,  1.59s/it]
actor_val:  39%|███▉      | 39/100 [01:08<01:41,  1.67s/it]
actor_val:  40%|████      | 40/100 [01:10<01:42,  1.71s/it]
actor_val:  41%|████      | 41/100 [01:11<01:38,  1.67s/it]
actor_val:  42%|████▏     | 42/100 [01:13<01:39,  1.72s/it]
actor_val:  43%|████▎     | 43/100 [01:15<01:34,  1.66s/it]
actor_val:  44%|████▍     | 44/100 [01:16<01:31,  1.64s/it]
actor_val:  45%|████▌     | 45/100 [01:18<01:28,  1.62s/it]
actor_val:  46%|████▌     | 46/100 [01:19<01:26,  1.59s/it]
actor_val:  47%|████▋     | 47/100 [01:21<01:24,  1.59s/it]
actor_val:  48%|████▊     | 48/100 [01:22<01:22,  1.58s/it]
actor_val:  49%|████▉     | 49/100 [01:24<01:20,  1.57s/it]
actor_val:  50%|█████     | 50/100 [01:26<01:23,  1.68s/it]
actor_val:  51%|█████     | 51/100 [01:28<01:24,  1.73s/it]
actor_val:  52%|█████▏    | 52/100 [01:29<01:20,  1.68s/it]
actor_val:  53%|█████▎    | 53/100 [01:31<01:21,  1.74s/it]
actor_val:  54%|█████▍    | 54/100 [01:33<01:21,  1.78s/it]
actor_val:  55%|█████▌    | 55/100 [01:35<01:17,  1.72s/it]
actor_val:  56%|█████▌    | 56/100 [01:36<01:14,  1.68s/it]
actor_val:  57%|█████▋    | 57/100 [01:38<01:10,  1.64s/it]
actor_val:  58%|█████▊    | 58/100 [01:39<01:07,  1.61s/it]
actor_val:  59%|█████▉    | 59/100 [01:41<01:05,  1.60s/it]
actor_val:  60%|██████    | 60/100 [01:42<01:03,  1.59s/it]
actor_val:  61%|██████    | 61/100 [01:44<01:04,  1.67s/it]
actor_val:  62%|██████▏   | 62/100 [01:46<01:02,  1.64s/it]
actor_val:  63%|██████▎   | 63/100 [01:47<00:59,  1.61s/it]
actor_val:  64%|██████▍   | 64/100 [01:49<00:57,  1.60s/it]
actor_val:  65%|██████▌   | 65/100 [01:51<00:55,  1.59s/it]
actor_val:  66%|██████▌   | 66/100 [01:52<00:56,  1.66s/it]
actor_val:  67%|██████▋   | 67/100 [01:54<00:53,  1.63s/it]
actor_val:  68%|██████▊   | 68/100 [01:55<00:51,  1.60s/it]
actor_val:  69%|██████▉   | 69/100 [01:57<00:49,  1.58s/it]
actor_val:  70%|███████   | 70/100 [01:59<00:47,  1.58s/it]
actor_val:  71%|███████   | 71/100 [02:00<00:45,  1.58s/it]
actor_val:  72%|███████▏  | 72/100 [02:02<00:44,  1.58s/it]
actor_val:  73%|███████▎  | 73/100 [02:04<00:44,  1.66s/it]
actor_val:  74%|███████▍  | 74/100 [02:05<00:42,  1.64s/it]
actor_val:  75%|███████▌  | 75/100 [02:07<00:40,  1.61s/it]
actor_val:  76%|███████▌  | 76/100 [02:08<00:38,  1.60s/it]
actor_val:  77%|███████▋  | 77/100 [02:10<00:36,  1.58s/it]
actor_val:  78%|███████▊  | 78/100 [02:11<00:34,  1.57s/it]
actor_val:  79%|███████▉  | 79/100 [02:13<00:33,  1.58s/it]
actor_val:  80%|████████  | 80/100 [02:15<00:31,  1.58s/it]
actor_val:  81%|████████  | 81/100 [02:16<00:29,  1.57s/it]
actor_val:  82%|████████▏ | 82/100 [02:18<00:29,  1.63s/it]
actor_val:  83%|████████▎ | 83/100 [02:20<00:28,  1.70s/it]
actor_val:  84%|████████▍ | 84/100 [02:22<00:27,  1.74s/it]
actor_val:  85%|████████▌ | 85/100 [02:23<00:26,  1.78s/it]
actor_val:  86%|████████▌ | 86/100 [02:25<00:23,  1.71s/it]
actor_val:  87%|████████▋ | 87/100 [02:26<00:21,  1.67s/it]
actor_val:  88%|████████▊ | 88/100 [02:28<00:19,  1.63s/it]
actor_val:  89%|████████▉ | 89/100 [02:30<00:17,  1.60s/it]
actor_val:  90%|█████████ | 90/100 [02:31<00:15,  1.59s/it]
actor_val:  91%|█████████ | 91/100 [02:33<00:14,  1.59s/it]
actor_val:  92%|█████████▏| 92/100 [02:35<00:13,  1.66s/it]
actor_val:  93%|█████████▎| 93/100 [02:36<00:12,  1.74s/it]
actor_val:  94%|█████████▍| 94/100 [02:38<00:10,  1.69s/it]
actor_val:  95%|█████████▌| 95/100 [02:40<00:08,  1.73s/it]
actor_val:  96%|█████████▌| 96/100 [02:41<00:06,  1.69s/it]
actor_val:  97%|█████████▋| 97/100 [02:43<00:04,  1.66s/it]
actor_val:  98%|█████████▊| 98/100 [02:45<00:03,  1.71s/it]
actor_val:  99%|█████████▉| 99/100 [02:47<00:01,  1.75s/it]
actor_val: 100%|██████████| 100/100 [02:48<00:00,  1.69s/it]
actor_val: 100%|██████████| 100/100 [02:48<00:00,  1.69s/it]
[2025-12-29 14:33:15] train_sft.py:658 - step 2000: {'val/loss': 0.4290625}
[2025-12-29 14:33:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:21] train_sft.py:596 - step 2000: {'train/loss': 0.01826401799917221, 'train/grad_norm': 0.3017100691795349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.15s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.15s/it]
[2025-12-29 14:33:21] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 14:33:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:26] train_sft.py:596 - step 2001: {'train/loss': 0.02297961711883545, 'train/grad_norm': 0.6947613954544067, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 14:33:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:33:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:30] train_sft.py:596 - step 2002: {'train/loss': 0.024511605501174927, 'train/grad_norm': 0.6305369734764099, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:33:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:33:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:35] train_sft.py:596 - step 2003: {'train/loss': 0.021669913083314896, 'train/grad_norm': 0.4668731093406677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 14:33:35] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:33:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:40] train_sft.py:596 - step 2004: {'train/loss': 0.028502002358436584, 'train/grad_norm': 0.5392651557922363, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:33:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:33:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:44] train_sft.py:596 - step 2005: {'train/loss': 0.024855852127075195, 'train/grad_norm': 0.4729522168636322, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:33:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:33:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:49] train_sft.py:596 - step 2006: {'train/loss': 0.02816268801689148, 'train/grad_norm': 0.73411625623703, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:33:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:33:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:54] train_sft.py:596 - step 2007: {'train/loss': 0.01883569359779358, 'train/grad_norm': 0.35481444001197815, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:33:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:33:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:33:59] train_sft.py:596 - step 2008: {'train/loss': 0.02597177028656006, 'train/grad_norm': 0.6082649827003479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:33:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:33:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:03] train_sft.py:596 - step 2009: {'train/loss': 0.0242537260055542, 'train/grad_norm': 0.5543094277381897, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:34:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:34:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:08] train_sft.py:596 - step 2010: {'train/loss': 0.021349258720874786, 'train/grad_norm': 0.44268888235092163, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:34:08] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:34:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:13] train_sft.py:596 - step 2011: {'train/loss': 0.022154659032821655, 'train/grad_norm': 0.41934964060783386, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 14:34:13] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:34:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:18] train_sft.py:596 - step 2012: {'train/loss': 0.028483539819717407, 'train/grad_norm': 0.5106149911880493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:34:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:34:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:23] train_sft.py:596 - step 2013: {'train/loss': 0.029750943183898926, 'train/grad_norm': 0.6648879647254944, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 14:34:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:34:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:28] train_sft.py:596 - step 2014: {'train/loss': 0.020864784717559814, 'train/grad_norm': 0.5206856727600098, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 14:34:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:34:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:33] train_sft.py:596 - step 2015: {'train/loss': 0.02270883321762085, 'train/grad_norm': 0.9045088887214661, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:34:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:34:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:37] train_sft.py:596 - step 2016: {'train/loss': 0.021481141448020935, 'train/grad_norm': 0.7551725506782532, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:34:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:34:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:42] train_sft.py:596 - step 2017: {'train/loss': 0.02501899003982544, 'train/grad_norm': 0.323832243680954, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:34:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:34:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:47] train_sft.py:596 - step 2018: {'train/loss': 0.02170431613922119, 'train/grad_norm': 0.48612263798713684, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:34:47] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:34:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:52] train_sft.py:596 - step 2019: {'train/loss': 0.018442004919052124, 'train/grad_norm': 0.39426910877227783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:34:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:34:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:34:57] train_sft.py:596 - step 2020: {'train/loss': 0.023471906781196594, 'train/grad_norm': 0.46329033374786377, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:34:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:34:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:02] train_sft.py:596 - step 2021: {'train/loss': 0.02661961317062378, 'train/grad_norm': 0.613459587097168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 14:35:02] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:35:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:07] train_sft.py:596 - step 2022: {'train/loss': 0.02529996633529663, 'train/grad_norm': 0.44478097558021545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 14:35:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:35:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:12] train_sft.py:596 - step 2023: {'train/loss': 0.021776586771011353, 'train/grad_norm': 0.41684457659721375, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:35:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:35:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:16] train_sft.py:596 - step 2024: {'train/loss': 0.03125154972076416, 'train/grad_norm': 0.45823347568511963, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:35:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:35:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:21] train_sft.py:596 - step 2025: {'train/loss': 0.024383336305618286, 'train/grad_norm': 0.6651031970977783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 14:35:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:35:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:26] train_sft.py:596 - step 2026: {'train/loss': 0.025811687111854553, 'train/grad_norm': 0.4169543385505676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:35:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:35:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:31] train_sft.py:596 - step 2027: {'train/loss': 0.023458585143089294, 'train/grad_norm': 0.5564600229263306, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 14:35:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:35:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:35] train_sft.py:596 - step 2028: {'train/loss': 0.011660099029541016, 'train/grad_norm': 0.32553574442863464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:35:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:35:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:40] train_sft.py:596 - step 2029: {'train/loss': 0.017722219228744507, 'train/grad_norm': 0.29562708735466003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:35:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:35:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:45] train_sft.py:596 - step 2030: {'train/loss': 0.0209665447473526, 'train/grad_norm': 0.316971093416214, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:35:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:35:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:50] train_sft.py:596 - step 2031: {'train/loss': 0.016619287431240082, 'train/grad_norm': 0.5057750940322876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:35:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:35:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:35:55] train_sft.py:596 - step 2032: {'train/loss': 0.014170095324516296, 'train/grad_norm': 0.4497460424900055, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 14:35:55] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:35:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:00] train_sft.py:596 - step 2033: {'train/loss': 0.02150796353816986, 'train/grad_norm': 0.472822368144989, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 14:36:00] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:36:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:05] train_sft.py:596 - step 2034: {'train/loss': 0.024320058524608612, 'train/grad_norm': 0.5388517379760742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 14:36:05] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:36:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:10] train_sft.py:596 - step 2035: {'train/loss': 0.012405306100845337, 'train/grad_norm': 0.32837095856666565, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:36:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:36:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:14] train_sft.py:596 - step 2036: {'train/loss': 0.017709732055664062, 'train/grad_norm': 0.435787171125412, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:36:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:36:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:19] train_sft.py:596 - step 2037: {'train/loss': 0.020187124609947205, 'train/grad_norm': 0.4950193166732788, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:36:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:36:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:24] train_sft.py:596 - step 2038: {'train/loss': 0.02001728117465973, 'train/grad_norm': 0.4229394793510437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 14:36:24] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:36:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:29] train_sft.py:596 - step 2039: {'train/loss': 0.020326077938079834, 'train/grad_norm': 0.4964926242828369, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:36:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:36:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:33] train_sft.py:596 - step 2040: {'train/loss': 0.02311265468597412, 'train/grad_norm': 0.4022147059440613, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:36:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:36:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:38] train_sft.py:596 - step 2041: {'train/loss': 0.024705171585083008, 'train/grad_norm': 0.539047360420227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:36:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:36:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:43] train_sft.py:596 - step 2042: {'train/loss': 0.023143768310546875, 'train/grad_norm': 0.47419190406799316, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:36:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:36:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:47] train_sft.py:596 - step 2043: {'train/loss': 0.017390161752700806, 'train/grad_norm': 0.5113323926925659, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:36:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:36:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:52] train_sft.py:596 - step 2044: {'train/loss': 0.01749083399772644, 'train/grad_norm': 0.3090575039386749, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:36:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:36:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:36:56] train_sft.py:596 - step 2045: {'train/loss': 0.024819225072860718, 'train/grad_norm': 1.0405765771865845, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:36:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:36:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:01] train_sft.py:596 - step 2046: {'train/loss': 0.013843581080436707, 'train/grad_norm': 0.3629875183105469, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:37:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:37:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:06] train_sft.py:596 - step 2047: {'train/loss': 0.02804332971572876, 'train/grad_norm': 0.6246369481086731, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:37:06] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:37:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:11] train_sft.py:596 - step 2048: {'train/loss': 0.01743549108505249, 'train/grad_norm': 0.5039689540863037, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 14:37:11] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:37:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:16] train_sft.py:596 - step 2049: {'train/loss': 0.03315095603466034, 'train/grad_norm': 1.2194112539291382, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:37:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:37:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:21] train_sft.py:596 - step 2050: {'train/loss': 0.010341823101043701, 'train/grad_norm': 0.25650954246520996, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:37:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:37:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:25] train_sft.py:596 - step 2051: {'train/loss': 0.013752996921539307, 'train/grad_norm': 0.5024600028991699, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:37:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:37:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:30] train_sft.py:596 - step 2052: {'train/loss': 0.021737724542617798, 'train/grad_norm': 0.6483979821205139, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:37:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:37:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:35] train_sft.py:596 - step 2053: {'train/loss': 0.014322519302368164, 'train/grad_norm': 0.3491586446762085, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 14:37:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:37:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:40] train_sft.py:596 - step 2054: {'train/loss': 0.02555146813392639, 'train/grad_norm': 0.7508449554443359, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 14:37:40] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:37:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:44] train_sft.py:596 - step 2055: {'train/loss': 0.02159292995929718, 'train/grad_norm': 0.4433804452419281, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:37:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:37:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:49] train_sft.py:596 - step 2056: {'train/loss': 0.026147983968257904, 'train/grad_norm': 0.6252244114875793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:37:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:37:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:54] train_sft.py:596 - step 2057: {'train/loss': 0.02181914448738098, 'train/grad_norm': 0.38741835951805115, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:37:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:37:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:37:59] train_sft.py:596 - step 2058: {'train/loss': 0.017417967319488525, 'train/grad_norm': 0.3832526206970215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 14:37:59] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:37:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:04] train_sft.py:596 - step 2059: {'train/loss': 0.018103718757629395, 'train/grad_norm': 0.43719619512557983, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:38:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:38:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:08] train_sft.py:596 - step 2060: {'train/loss': 0.012722373008728027, 'train/grad_norm': 0.26007911562919617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:38:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:38:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:13] train_sft.py:596 - step 2061: {'train/loss': 0.021966606378555298, 'train/grad_norm': 1.2369616031646729, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:38:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:38:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:18] train_sft.py:596 - step 2062: {'train/loss': 0.018161609768867493, 'train/grad_norm': 0.36370590329170227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:38:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:38:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:23] train_sft.py:596 - step 2063: {'train/loss': 0.016045615077018738, 'train/grad_norm': 0.4044698178768158, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:38:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:38:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:28] train_sft.py:596 - step 2064: {'train/loss': 0.02388058975338936, 'train/grad_norm': 0.5047188997268677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 14:38:28] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:38:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:33] train_sft.py:596 - step 2065: {'train/loss': 0.018406927585601807, 'train/grad_norm': 0.6676871180534363, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:38:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:38:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:37] train_sft.py:596 - step 2066: {'train/loss': 0.022191926836967468, 'train/grad_norm': 0.47713616490364075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 14:38:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:38:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:42] train_sft.py:596 - step 2067: {'train/loss': 0.01421460509300232, 'train/grad_norm': 0.3579018712043762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:38:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:38:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:47] train_sft.py:596 - step 2068: {'train/loss': 0.017637446522712708, 'train/grad_norm': 0.43914511799812317, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:38:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:38:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:51] train_sft.py:596 - step 2069: {'train/loss': 0.01801489293575287, 'train/grad_norm': 0.5223737955093384, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:38:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:38:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:38:56] train_sft.py:596 - step 2070: {'train/loss': 0.017446517944335938, 'train/grad_norm': 0.3742780387401581, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:38:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:38:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:01] train_sft.py:596 - step 2071: {'train/loss': 0.019137486815452576, 'train/grad_norm': 0.48417940735816956, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:39:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:39:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:05] train_sft.py:596 - step 2072: {'train/loss': 0.015231534838676453, 'train/grad_norm': 0.5312747955322266, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:39:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:10] train_sft.py:596 - step 2073: {'train/loss': 0.02641502022743225, 'train/grad_norm': 0.4621042311191559, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:39:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:14] train_sft.py:596 - step 2074: {'train/loss': 0.016702044755220413, 'train/grad_norm': 0.46273455023765564, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 14:39:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:39:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:19] train_sft.py:596 - step 2075: {'train/loss': 0.01680605113506317, 'train/grad_norm': 0.37034371495246887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:39:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:24] train_sft.py:596 - step 2076: {'train/loss': 0.015392541885375977, 'train/grad_norm': 0.31029564142227173, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:39:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:28] train_sft.py:596 - step 2077: {'train/loss': 0.02093885838985443, 'train/grad_norm': 0.6708547472953796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:39:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:33] train_sft.py:596 - step 2078: {'train/loss': 0.017099320888519287, 'train/grad_norm': 0.5833742022514343, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:39:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:38] train_sft.py:596 - step 2079: {'train/loss': 0.024077236652374268, 'train/grad_norm': 0.5155143737792969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:39:38] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:39:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:42] train_sft.py:596 - step 2080: {'train/loss': 0.018812764436006546, 'train/grad_norm': 0.4272860288619995, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:39:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:47] train_sft.py:596 - step 2081: {'train/loss': 0.01667606830596924, 'train/grad_norm': 0.4203141927719116, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:39:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:52] train_sft.py:596 - step 2082: {'train/loss': 0.020972270518541336, 'train/grad_norm': 0.4563924968242645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:39:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:39:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:39:56] train_sft.py:596 - step 2083: {'train/loss': 0.026032432913780212, 'train/grad_norm': 0.6646723747253418, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:39:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:39:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:01] train_sft.py:596 - step 2084: {'train/loss': 0.013260483741760254, 'train/grad_norm': 0.3302723169326782, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:40:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:06] train_sft.py:596 - step 2085: {'train/loss': 0.019943609833717346, 'train/grad_norm': 0.6224823594093323, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:40:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:40:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:10] train_sft.py:596 - step 2086: {'train/loss': 0.01713523268699646, 'train/grad_norm': 0.5582947731018066, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:40:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:40:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:15] train_sft.py:596 - step 2087: {'train/loss': 0.018122106790542603, 'train/grad_norm': 0.5203081965446472, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:40:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:40:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:19] train_sft.py:596 - step 2088: {'train/loss': 0.022909089922904968, 'train/grad_norm': 0.4990340769290924, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:40:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:24] train_sft.py:596 - step 2089: {'train/loss': 0.024901196360588074, 'train/grad_norm': 1.09819495677948, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:40:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:40:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:29] train_sft.py:596 - step 2090: {'train/loss': 0.015140101313591003, 'train/grad_norm': 0.3126586377620697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:40:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:34] train_sft.py:596 - step 2091: {'train/loss': 0.016318723559379578, 'train/grad_norm': 0.518562912940979, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:40:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:38] train_sft.py:596 - step 2092: {'train/loss': 0.0159604549407959, 'train/grad_norm': 0.24717466533184052, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:40:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:40:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:43] train_sft.py:596 - step 2093: {'train/loss': 0.020616598427295685, 'train/grad_norm': 0.5236256122589111, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:40:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:48] train_sft.py:596 - step 2094: {'train/loss': 0.018582478165626526, 'train/grad_norm': 0.5134433507919312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:40:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:52] train_sft.py:596 - step 2095: {'train/loss': 0.02563653141260147, 'train/grad_norm': 0.7445036768913269, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:40:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:40:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:40:57] train_sft.py:596 - step 2096: {'train/loss': 0.014439672231674194, 'train/grad_norm': 0.40582022070884705, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:40:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:40:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:02] train_sft.py:596 - step 2097: {'train/loss': 0.01634841412305832, 'train/grad_norm': 0.38463294506073, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:41:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:06] train_sft.py:596 - step 2098: {'train/loss': 0.021238550543785095, 'train/grad_norm': 0.7964668869972229, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:41:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:11] train_sft.py:596 - step 2099: {'train/loss': 0.0210171639919281, 'train/grad_norm': 0.41401782631874084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:41:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:16] train_sft.py:596 - step 2100: {'train/loss': 0.017164483666419983, 'train/grad_norm': 0.4605104625225067, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:41:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:41:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:20] train_sft.py:596 - step 2101: {'train/loss': 0.02273428440093994, 'train/grad_norm': 0.6633170247077942, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:41:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:41:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:25] train_sft.py:596 - step 2102: {'train/loss': 0.02384309470653534, 'train/grad_norm': 0.5397909879684448, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:41:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:29] train_sft.py:596 - step 2103: {'train/loss': 0.015341103076934814, 'train/grad_norm': 0.6353863477706909, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:41:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:34] train_sft.py:596 - step 2104: {'train/loss': 0.01655096746981144, 'train/grad_norm': 0.5669901967048645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:41:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:39] train_sft.py:596 - step 2105: {'train/loss': 0.021778970956802368, 'train/grad_norm': 0.4683786928653717, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:41:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:41:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:44] train_sft.py:596 - step 2106: {'train/loss': 0.02436649799346924, 'train/grad_norm': 0.5045670866966248, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:41:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:41:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:49] train_sft.py:596 - step 2107: {'train/loss': 0.021412715315818787, 'train/grad_norm': 0.7596027851104736, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 14:41:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:41:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:53] train_sft.py:596 - step 2108: {'train/loss': 0.015112437307834625, 'train/grad_norm': 0.3550044298171997, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:41:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:41:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:41:58] train_sft.py:596 - step 2109: {'train/loss': 0.025000959634780884, 'train/grad_norm': 0.6606184244155884, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:41:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:41:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:02] train_sft.py:596 - step 2110: {'train/loss': 0.02080923318862915, 'train/grad_norm': 0.3571451008319855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:42:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:42:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:07] train_sft.py:596 - step 2111: {'train/loss': 0.020068690180778503, 'train/grad_norm': 0.6109620332717896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:42:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:42:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:12] train_sft.py:596 - step 2112: {'train/loss': 0.02795754373073578, 'train/grad_norm': 0.6145030856132507, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:42:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:42:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:16] train_sft.py:596 - step 2113: {'train/loss': 0.020437419414520264, 'train/grad_norm': 0.41761237382888794, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:42:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:42:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:21] train_sft.py:596 - step 2114: {'train/loss': 0.027260899543762207, 'train/grad_norm': 0.45658302307128906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:42:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:42:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:26] train_sft.py:596 - step 2115: {'train/loss': 0.0542110800743103, 'train/grad_norm': 0.5774060487747192, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:42:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:42:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:30] train_sft.py:596 - step 2116: {'train/loss': 0.016072869300842285, 'train/grad_norm': 0.3640304505825043, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:42:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:42:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:35] train_sft.py:596 - step 2117: {'train/loss': 0.025498300790786743, 'train/grad_norm': 0.7722774744033813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:42:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:42:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:40] train_sft.py:596 - step 2118: {'train/loss': 0.026139020919799805, 'train/grad_norm': 0.4637477993965149, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 14:42:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:42:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:45] train_sft.py:596 - step 2119: {'train/loss': 0.01590082049369812, 'train/grad_norm': 0.3036010265350342, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:42:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:42:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:50] train_sft.py:596 - step 2120: {'train/loss': 0.019656747579574585, 'train/grad_norm': 0.5375601053237915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 14:42:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:42:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:42:55] train_sft.py:596 - step 2121: {'train/loss': 0.025060951709747314, 'train/grad_norm': 0.7696370482444763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 14:42:55] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 14:42:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:00] train_sft.py:596 - step 2122: {'train/loss': 0.012667745351791382, 'train/grad_norm': 0.3300042152404785, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:43:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:43:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:05] train_sft.py:596 - step 2123: {'train/loss': 0.023458808660507202, 'train/grad_norm': 0.506773829460144, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:43:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:43:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:10] train_sft.py:596 - step 2124: {'train/loss': 0.02025941014289856, 'train/grad_norm': 0.4275210201740265, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:43:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:43:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:15] train_sft.py:596 - step 2125: {'train/loss': 0.014750868082046509, 'train/grad_norm': 0.48511606454849243, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:43:15] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:43:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:19] train_sft.py:596 - step 2126: {'train/loss': 0.0165034681558609, 'train/grad_norm': 0.2885892987251282, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:43:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:43:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:24] train_sft.py:596 - step 2127: {'train/loss': 0.028293728828430176, 'train/grad_norm': 0.9729083776473999, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:43:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:43:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:29] train_sft.py:596 - step 2128: {'train/loss': 0.02307027578353882, 'train/grad_norm': 0.5273345112800598, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:43:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:43:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:34] train_sft.py:596 - step 2129: {'train/loss': 0.020210161805152893, 'train/grad_norm': 0.3886792063713074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:43:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:43:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:39] train_sft.py:596 - step 2130: {'train/loss': 0.017720118165016174, 'train/grad_norm': 0.4832268953323364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 14:43:39] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:43:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:44] train_sft.py:596 - step 2131: {'train/loss': 0.02174154669046402, 'train/grad_norm': 0.7194443345069885, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:43:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:43:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:49] train_sft.py:596 - step 2132: {'train/loss': 0.013610228896141052, 'train/grad_norm': 0.4107820689678192, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:43:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:43:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:53] train_sft.py:596 - step 2133: {'train/loss': 0.02948717772960663, 'train/grad_norm': 0.9035197496414185, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:43:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:43:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:43:58] train_sft.py:596 - step 2134: {'train/loss': 0.020628035068511963, 'train/grad_norm': 0.4198918044567108, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 14:43:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:43:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:03] train_sft.py:596 - step 2135: {'train/loss': 0.027125000953674316, 'train/grad_norm': 0.6049050092697144, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:44:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:44:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:07] train_sft.py:596 - step 2136: {'train/loss': 0.03084266185760498, 'train/grad_norm': 1.0243481397628784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 14:44:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:44:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:12] train_sft.py:596 - step 2137: {'train/loss': 0.019678838551044464, 'train/grad_norm': 0.4408526122570038, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:44:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:44:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:17] train_sft.py:596 - step 2138: {'train/loss': 0.019409701228141785, 'train/grad_norm': 0.42378512024879456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 14:44:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:44:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:22] train_sft.py:596 - step 2139: {'train/loss': 0.02771848440170288, 'train/grad_norm': 0.37394410371780396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 14:44:22] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:44:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:26] train_sft.py:596 - step 2140: {'train/loss': 0.017463048920035362, 'train/grad_norm': 0.422561913728714, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:44:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:44:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:31] train_sft.py:596 - step 2141: {'train/loss': 0.023854762315750122, 'train/grad_norm': 0.47457870841026306, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:44:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:44:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:36] train_sft.py:596 - step 2142: {'train/loss': 0.021436214447021484, 'train/grad_norm': 0.487192839384079, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:44:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:44:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:41] train_sft.py:596 - step 2143: {'train/loss': 0.026548683643341064, 'train/grad_norm': 0.46878740191459656, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:44:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:44:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:46] train_sft.py:596 - step 2144: {'train/loss': 0.012020502239465714, 'train/grad_norm': 0.27940478920936584, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:44:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:44:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:50] train_sft.py:596 - step 2145: {'train/loss': 0.018616676330566406, 'train/grad_norm': 0.6784372329711914, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:44:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:44:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:44:55] train_sft.py:596 - step 2146: {'train/loss': 0.018095295876264572, 'train/grad_norm': 0.4319903254508972, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:44:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:44:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:01] train_sft.py:596 - step 2147: {'train/loss': 0.021326661109924316, 'train/grad_norm': 0.49765029549598694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 14:45:01] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:45:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:05] train_sft.py:596 - step 2148: {'train/loss': 0.02492627501487732, 'train/grad_norm': 0.522860586643219, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:45:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:45:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:11] train_sft.py:596 - step 2149: {'train/loss': 0.016649693250656128, 'train/grad_norm': 0.3492164611816406, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 14:45:11] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:45:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:16] train_sft.py:596 - step 2150: {'train/loss': 0.02009052038192749, 'train/grad_norm': 0.7267064452171326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:45:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:45:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:21] train_sft.py:596 - step 2151: {'train/loss': 0.019047260284423828, 'train/grad_norm': 0.42558497190475464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:45:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:45:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:26] train_sft.py:596 - step 2152: {'train/loss': 0.01973557472229004, 'train/grad_norm': 0.4395610988140106, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 14:45:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:45:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:30] train_sft.py:596 - step 2153: {'train/loss': 0.021747052669525146, 'train/grad_norm': 0.55467689037323, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:45:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:45:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:35] train_sft.py:596 - step 2154: {'train/loss': 0.021859169006347656, 'train/grad_norm': 0.40506041049957275, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:45:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:45:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:40] train_sft.py:596 - step 2155: {'train/loss': 0.016377687454223633, 'train/grad_norm': 0.3688233196735382, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:45:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:45:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:44] train_sft.py:596 - step 2156: {'train/loss': 0.0249621719121933, 'train/grad_norm': 0.5333049893379211, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:45:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:45:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:49] train_sft.py:596 - step 2157: {'train/loss': 0.019176341593265533, 'train/grad_norm': 0.5313109159469604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:45:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:45:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:54] train_sft.py:596 - step 2158: {'train/loss': 0.020781487226486206, 'train/grad_norm': 0.5610402226448059, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:45:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:45:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:45:58] train_sft.py:596 - step 2159: {'train/loss': 0.023653730750083923, 'train/grad_norm': 0.6637714505195618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:45:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:45:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:03] train_sft.py:596 - step 2160: {'train/loss': 0.022510305047035217, 'train/grad_norm': 0.44656386971473694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:46:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:46:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:07] train_sft.py:596 - step 2161: {'train/loss': 0.020093321800231934, 'train/grad_norm': 0.4128154516220093, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:46:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:46:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:12] train_sft.py:596 - step 2162: {'train/loss': 0.02554638683795929, 'train/grad_norm': 0.5199885368347168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:46:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:46:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:17] train_sft.py:596 - step 2163: {'train/loss': 0.025287315249443054, 'train/grad_norm': 0.5296548008918762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:46:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:46:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:22] train_sft.py:596 - step 2164: {'train/loss': 0.02605205774307251, 'train/grad_norm': 0.4672306776046753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:46:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:46:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:26] train_sft.py:596 - step 2165: {'train/loss': 0.015430629253387451, 'train/grad_norm': 0.44595402479171753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 14:46:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:46:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:31] train_sft.py:596 - step 2166: {'train/loss': 0.023456159979104996, 'train/grad_norm': 0.7896262407302856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:46:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:46:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:36] train_sft.py:596 - step 2167: {'train/loss': 0.019742101430892944, 'train/grad_norm': 0.6444059014320374, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 14:46:36] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:46:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:42] train_sft.py:596 - step 2168: {'train/loss': 0.022911787033081055, 'train/grad_norm': 0.5366446375846863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 14:46:42] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:46:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:46] train_sft.py:596 - step 2169: {'train/loss': 0.030156288295984268, 'train/grad_norm': 0.6916738152503967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:46:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:46:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:52] train_sft.py:596 - step 2170: {'train/loss': 0.020893633365631104, 'train/grad_norm': 0.6921567916870117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
[2025-12-29 14:46:52] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 14:46:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:46:57] train_sft.py:596 - step 2171: {'train/loss': 0.026441119611263275, 'train/grad_norm': 0.420536607503891, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:46:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:46:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:02] train_sft.py:596 - step 2172: {'train/loss': 0.026583075523376465, 'train/grad_norm': 0.6100879907608032, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:47:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:47:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:07] train_sft.py:596 - step 2173: {'train/loss': 0.015082031488418579, 'train/grad_norm': 0.441434383392334, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:47:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:47:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:11] train_sft.py:596 - step 2174: {'train/loss': 0.021587342023849487, 'train/grad_norm': 0.3391452729701996, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:47:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:47:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:16] train_sft.py:596 - step 2175: {'train/loss': 0.014459495432674885, 'train/grad_norm': 0.49690985679626465, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:47:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:47:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:21] train_sft.py:596 - step 2176: {'train/loss': 0.023136407136917114, 'train/grad_norm': 0.5551098585128784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.21s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.21s/it]
[2025-12-29 14:47:21] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 14:47:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:26] train_sft.py:596 - step 2177: {'train/loss': 0.02375161647796631, 'train/grad_norm': 0.4465908110141754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:47:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:47:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:31] train_sft.py:596 - step 2178: {'train/loss': 0.032774344086647034, 'train/grad_norm': 0.9141069650650024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 14:47:31] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:47:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:36] train_sft.py:596 - step 2179: {'train/loss': 0.02268773317337036, 'train/grad_norm': 0.48755037784576416, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:47:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:47:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:41] train_sft.py:596 - step 2180: {'train/loss': 0.01538875699043274, 'train/grad_norm': 0.4119904637336731, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:47:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:47:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:45] train_sft.py:596 - step 2181: {'train/loss': 0.022103741765022278, 'train/grad_norm': 0.4743928611278534, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:47:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:47:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:51] train_sft.py:596 - step 2182: {'train/loss': 0.02204573154449463, 'train/grad_norm': 0.4270804524421692, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
[2025-12-29 14:47:51] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 14:47:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:47:56] train_sft.py:596 - step 2183: {'train/loss': 0.01886371709406376, 'train/grad_norm': 0.5858349800109863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:47:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:47:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:00] train_sft.py:596 - step 2184: {'train/loss': 0.02976316213607788, 'train/grad_norm': 0.7064605951309204, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:48:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:48:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:06] train_sft.py:596 - step 2185: {'train/loss': 0.026689648628234863, 'train/grad_norm': 0.6241757869720459, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 14:48:06] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:48:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:10] train_sft.py:596 - step 2186: {'train/loss': 0.024560344871133566, 'train/grad_norm': 0.4561200439929962, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:48:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:48:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:15] train_sft.py:596 - step 2187: {'train/loss': 0.02779538929462433, 'train/grad_norm': 0.6841648817062378, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 14:48:15] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:48:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:20] train_sft.py:596 - step 2188: {'train/loss': 0.04806947708129883, 'train/grad_norm': 1.3583279848098755, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:48:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:48:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:25] train_sft.py:596 - step 2189: {'train/loss': 0.015026338398456573, 'train/grad_norm': 0.3880411386489868, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 14:48:25] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:48:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:30] train_sft.py:596 - step 2190: {'train/loss': 0.019888639450073242, 'train/grad_norm': 0.5373886227607727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 14:48:30] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:48:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:35] train_sft.py:596 - step 2191: {'train/loss': 0.017263323068618774, 'train/grad_norm': 0.44252878427505493, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:48:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:48:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:40] train_sft.py:596 - step 2192: {'train/loss': 0.036650434136390686, 'train/grad_norm': 1.625060796737671, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 14:48:40] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:48:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:45] train_sft.py:596 - step 2193: {'train/loss': 0.02359449863433838, 'train/grad_norm': 0.4978207051753998, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:48:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:48:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:50] train_sft.py:596 - step 2194: {'train/loss': 0.03135162591934204, 'train/grad_norm': 0.6300050020217896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:48:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:48:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:55] train_sft.py:596 - step 2195: {'train/loss': 0.022457659244537354, 'train/grad_norm': 0.49110081791877747, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 14:48:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:48:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:48:59] train_sft.py:596 - step 2196: {'train/loss': 0.026306629180908203, 'train/grad_norm': 0.5147573351860046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:48:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:49:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:04] train_sft.py:596 - step 2197: {'train/loss': 0.02768738567829132, 'train/grad_norm': 0.8746880292892456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:49:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:49:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:09] train_sft.py:596 - step 2198: {'train/loss': 0.01990610361099243, 'train/grad_norm': 0.3556329309940338, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:49:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:49:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:13] train_sft.py:596 - step 2199: {'train/loss': 0.02504327893257141, 'train/grad_norm': 0.5501618981361389, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 14:49:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:49:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:18] train_sft.py:596 - step 2200: {'train/loss': 0.023026585578918457, 'train/grad_norm': 0.507175087928772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 14:49:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:49:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:23] train_sft.py:596 - step 2201: {'train/loss': 0.025394044816493988, 'train/grad_norm': 0.7365293502807617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:49:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:49:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:28] train_sft.py:596 - step 2202: {'train/loss': 0.024366378784179688, 'train/grad_norm': 0.44097986817359924, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:49:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:49:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:33] train_sft.py:596 - step 2203: {'train/loss': 0.03685794770717621, 'train/grad_norm': 1.3818690776824951, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:49:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:49:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:37] train_sft.py:596 - step 2204: {'train/loss': 0.020560026168823242, 'train/grad_norm': 0.5837798714637756, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 14:49:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:49:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:42] train_sft.py:596 - step 2205: {'train/loss': 0.019809454679489136, 'train/grad_norm': 0.5262015461921692, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:49:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:49:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:47] train_sft.py:596 - step 2206: {'train/loss': 0.018002033233642578, 'train/grad_norm': 0.6528557538986206, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:49:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:49:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:51] train_sft.py:596 - step 2207: {'train/loss': 0.022105246782302856, 'train/grad_norm': 0.6124969720840454, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:49:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:49:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:49:56] train_sft.py:596 - step 2208: {'train/loss': 0.01963922381401062, 'train/grad_norm': 0.5076209902763367, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:49:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:49:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:01] train_sft.py:596 - step 2209: {'train/loss': 0.022566914558410645, 'train/grad_norm': 0.580007791519165, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:50:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:50:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:06] train_sft.py:596 - step 2210: {'train/loss': 0.020884208381175995, 'train/grad_norm': 0.6104187965393066, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 14:50:06] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:50:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:11] train_sft.py:596 - step 2211: {'train/loss': 0.02540692687034607, 'train/grad_norm': 0.6266541481018066, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:50:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:50:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:16] train_sft.py:596 - step 2212: {'train/loss': 0.02188459038734436, 'train/grad_norm': 0.5017428994178772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:50:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:50:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:21] train_sft.py:596 - step 2213: {'train/loss': 0.02366238832473755, 'train/grad_norm': 0.4275840222835541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 14:50:21] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:50:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:26] train_sft.py:596 - step 2214: {'train/loss': 0.027745366096496582, 'train/grad_norm': 0.7020586729049683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 14:50:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:50:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:31] train_sft.py:596 - step 2215: {'train/loss': 0.02360665798187256, 'train/grad_norm': 0.7721848487854004, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:50:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:50:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:35] train_sft.py:596 - step 2216: {'train/loss': 0.03250570595264435, 'train/grad_norm': 0.639214813709259, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:50:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:50:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:40] train_sft.py:596 - step 2217: {'train/loss': 0.02372729778289795, 'train/grad_norm': 0.6470531225204468, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:50:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:50:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:45] train_sft.py:596 - step 2218: {'train/loss': 0.02290862798690796, 'train/grad_norm': 0.5594276785850525, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 14:50:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:50:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:50] train_sft.py:596 - step 2219: {'train/loss': 0.02077186107635498, 'train/grad_norm': 0.6240267753601074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:50:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:50:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:50:55] train_sft.py:596 - step 2220: {'train/loss': 0.030042335391044617, 'train/grad_norm': 1.7297972440719604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 14:50:55] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:50:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:00] train_sft.py:596 - step 2221: {'train/loss': 0.019447043538093567, 'train/grad_norm': 0.4585842788219452, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 14:51:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:51:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:05] train_sft.py:596 - step 2222: {'train/loss': 0.018868103623390198, 'train/grad_norm': 0.3945150375366211, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:51:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:51:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:09] train_sft.py:596 - step 2223: {'train/loss': 0.023881196975708008, 'train/grad_norm': 0.6362171769142151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:51:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:51:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:14] train_sft.py:596 - step 2224: {'train/loss': 0.031207501888275146, 'train/grad_norm': 0.6101957559585571, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:51:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:51:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:18] train_sft.py:596 - step 2225: {'train/loss': 0.029591664671897888, 'train/grad_norm': 0.610579252243042, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:51:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:51:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:23] train_sft.py:596 - step 2226: {'train/loss': 0.02817678451538086, 'train/grad_norm': 0.6306918859481812, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:51:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:51:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:28] train_sft.py:596 - step 2227: {'train/loss': 0.020521879196166992, 'train/grad_norm': 0.9378072619438171, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:51:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:51:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:33] train_sft.py:596 - step 2228: {'train/loss': 0.025932788848876953, 'train/grad_norm': 0.6569825410842896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 14:51:33] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:51:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:38] train_sft.py:596 - step 2229: {'train/loss': 0.04061770439147949, 'train/grad_norm': 0.6209431886672974, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:51:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:51:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:42] train_sft.py:596 - step 2230: {'train/loss': 0.022493712604045868, 'train/grad_norm': 0.5623483061790466, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 14:51:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:51:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:47] train_sft.py:596 - step 2231: {'train/loss': 0.03843855857849121, 'train/grad_norm': 0.7595333456993103, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:51:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:51:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:51] train_sft.py:596 - step 2232: {'train/loss': 0.0231611430644989, 'train/grad_norm': 0.5824355483055115, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:51:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:51:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:51:56] train_sft.py:596 - step 2233: {'train/loss': 0.021638989448547363, 'train/grad_norm': 0.49547910690307617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:51:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:51:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:01] train_sft.py:596 - step 2234: {'train/loss': 0.021690785884857178, 'train/grad_norm': 0.43322035670280457, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 14:52:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:52:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:06] train_sft.py:596 - step 2235: {'train/loss': 0.024117767810821533, 'train/grad_norm': 0.4368384778499603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:52:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:52:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:11] train_sft.py:596 - step 2236: {'train/loss': 0.028047680854797363, 'train/grad_norm': 0.9588096737861633, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 14:52:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:52:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:15] train_sft.py:596 - step 2237: {'train/loss': 0.01913142204284668, 'train/grad_norm': 0.4547423720359802, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:52:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:52:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:20] train_sft.py:596 - step 2238: {'train/loss': 0.02453380823135376, 'train/grad_norm': 0.577767014503479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:52:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:52:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:25] train_sft.py:596 - step 2239: {'train/loss': 0.024356961250305176, 'train/grad_norm': 0.37282291054725647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:52:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:52:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:30] train_sft.py:596 - step 2240: {'train/loss': 0.02459295094013214, 'train/grad_norm': 0.5377089381217957, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:52:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:52:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:34] train_sft.py:596 - step 2241: {'train/loss': 0.0158197283744812, 'train/grad_norm': 0.2954479455947876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:52:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:52:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:39] train_sft.py:596 - step 2242: {'train/loss': 0.025258123874664307, 'train/grad_norm': 0.5329747200012207, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:52:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:52:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:43] train_sft.py:596 - step 2243: {'train/loss': 0.023125767707824707, 'train/grad_norm': 0.40319186449050903, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:52:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:52:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:48] train_sft.py:596 - step 2244: {'train/loss': 0.025989264249801636, 'train/grad_norm': 0.6435108184814453, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 14:52:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:52:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:53] train_sft.py:596 - step 2245: {'train/loss': 0.025546520948410034, 'train/grad_norm': 0.48275843262672424, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:52:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:52:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:52:58] train_sft.py:596 - step 2246: {'train/loss': 0.026254065334796906, 'train/grad_norm': 0.5160506367683411, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:52:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:52:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:02] train_sft.py:596 - step 2247: {'train/loss': 0.023147404193878174, 'train/grad_norm': 0.6597267985343933, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:53:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:53:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:07] train_sft.py:596 - step 2248: {'train/loss': 0.024564415216445923, 'train/grad_norm': 0.555966854095459, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:53:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:53:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:12] train_sft.py:596 - step 2249: {'train/loss': 0.023910164833068848, 'train/grad_norm': 0.36973631381988525, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:53:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:53:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:16] train_sft.py:596 - step 2250: {'train/loss': 0.028561115264892578, 'train/grad_norm': 0.7973707914352417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:53:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:53:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:21] train_sft.py:596 - step 2251: {'train/loss': 0.03338360786437988, 'train/grad_norm': 0.647288978099823, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:53:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:53:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:26] train_sft.py:596 - step 2252: {'train/loss': 0.024503961205482483, 'train/grad_norm': 0.619387149810791, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 14:53:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:53:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:31] train_sft.py:596 - step 2253: {'train/loss': 0.01615220308303833, 'train/grad_norm': 0.38205841183662415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 14:53:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:53:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:36] train_sft.py:596 - step 2254: {'train/loss': 0.014112502336502075, 'train/grad_norm': 0.40423285961151123, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:53:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:53:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:40] train_sft.py:596 - step 2255: {'train/loss': 0.01647573709487915, 'train/grad_norm': 0.825869083404541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:53:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:53:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:45] train_sft.py:596 - step 2256: {'train/loss': 0.021164700388908386, 'train/grad_norm': 0.6051486730575562, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:53:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:53:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:50] train_sft.py:596 - step 2257: {'train/loss': 0.01653417944908142, 'train/grad_norm': 0.29257506132125854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:53:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:53:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:54] train_sft.py:596 - step 2258: {'train/loss': 0.01584203541278839, 'train/grad_norm': 0.4874538481235504, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:53:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:53:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:53:59] train_sft.py:596 - step 2259: {'train/loss': 0.014862626791000366, 'train/grad_norm': 0.3164280354976654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 14:53:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:53:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:04] train_sft.py:596 - step 2260: {'train/loss': 0.016232341527938843, 'train/grad_norm': 0.4763515293598175, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:54:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:54:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:08] train_sft.py:596 - step 2261: {'train/loss': 0.015590401366353035, 'train/grad_norm': 0.6004678606987, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:54:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:54:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:13] train_sft.py:596 - step 2262: {'train/loss': 0.017915192991495132, 'train/grad_norm': 0.5330206751823425, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 14:54:13] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:54:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:18] train_sft.py:596 - step 2263: {'train/loss': 0.021701693534851074, 'train/grad_norm': 0.6675641536712646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:54:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:54:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:23] train_sft.py:596 - step 2264: {'train/loss': 0.017388872802257538, 'train/grad_norm': 0.5237115621566772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:54:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:54:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:28] train_sft.py:596 - step 2265: {'train/loss': 0.015817716717720032, 'train/grad_norm': 0.682460606098175, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:54:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:54:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:32] train_sft.py:596 - step 2266: {'train/loss': 0.021658509969711304, 'train/grad_norm': 0.6338704824447632, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:54:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:54:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:37] train_sft.py:596 - step 2267: {'train/loss': 0.01740216463804245, 'train/grad_norm': 0.4798056483268738, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 14:54:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:54:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:42] train_sft.py:596 - step 2268: {'train/loss': 0.009668847545981407, 'train/grad_norm': 0.26822754740715027, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:54:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:54:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:46] train_sft.py:596 - step 2269: {'train/loss': 0.015922315418720245, 'train/grad_norm': 0.6547203660011292, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 14:54:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:54:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:51] train_sft.py:596 - step 2270: {'train/loss': 0.02032993733882904, 'train/grad_norm': 0.8005703687667847, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 14:54:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:54:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:54:55] train_sft.py:596 - step 2271: {'train/loss': 0.025881830602884293, 'train/grad_norm': 1.568901777267456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 14:54:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:54:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:00] train_sft.py:596 - step 2272: {'train/loss': 0.012519806623458862, 'train/grad_norm': 0.4961521327495575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:55:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:05] train_sft.py:596 - step 2273: {'train/loss': 0.020949214696884155, 'train/grad_norm': 0.5765897631645203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:55:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:09] train_sft.py:596 - step 2274: {'train/loss': 0.01758650690317154, 'train/grad_norm': 0.45225560665130615, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:55:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:14] train_sft.py:596 - step 2275: {'train/loss': 0.011254414916038513, 'train/grad_norm': 0.36887118220329285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:55:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:19] train_sft.py:596 - step 2276: {'train/loss': 0.015375319868326187, 'train/grad_norm': 0.39129170775413513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:55:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:55:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:23] train_sft.py:596 - step 2277: {'train/loss': 0.02191856876015663, 'train/grad_norm': 1.0299896001815796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:55:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:55:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:28] train_sft.py:596 - step 2278: {'train/loss': 0.010745853185653687, 'train/grad_norm': 0.408355176448822, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 14:55:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:55:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:32] train_sft.py:596 - step 2279: {'train/loss': 0.018475204706192017, 'train/grad_norm': 0.8156428933143616, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:55:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:37] train_sft.py:596 - step 2280: {'train/loss': 0.014370113611221313, 'train/grad_norm': 0.5828453302383423, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:55:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:42] train_sft.py:596 - step 2281: {'train/loss': 0.014199435710906982, 'train/grad_norm': 0.3712492287158966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:55:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:47] train_sft.py:596 - step 2282: {'train/loss': 0.020626641809940338, 'train/grad_norm': 0.3998030424118042, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:55:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:55:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:51] train_sft.py:596 - step 2283: {'train/loss': 0.016074344515800476, 'train/grad_norm': 0.36733099818229675, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:55:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:55:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:55:56] train_sft.py:596 - step 2284: {'train/loss': 0.022519446909427643, 'train/grad_norm': 0.5553326606750488, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 14:55:56] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:55:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:01] train_sft.py:596 - step 2285: {'train/loss': 0.01701252907514572, 'train/grad_norm': 0.5707810521125793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:56:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:56:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:05] train_sft.py:596 - step 2286: {'train/loss': 0.012123357504606247, 'train/grad_norm': 0.5097193717956543, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:56:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:56:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:10] train_sft.py:596 - step 2287: {'train/loss': 0.02603626251220703, 'train/grad_norm': 0.6854045391082764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:56:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:56:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:15] train_sft.py:596 - step 2288: {'train/loss': 0.025044500827789307, 'train/grad_norm': 0.7560077905654907, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:56:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:56:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:19] train_sft.py:596 - step 2289: {'train/loss': 0.018572106957435608, 'train/grad_norm': 0.4438048303127289, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 14:56:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:56:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:24] train_sft.py:596 - step 2290: {'train/loss': 0.01908355951309204, 'train/grad_norm': 0.4079855978488922, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 14:56:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:56:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:28] train_sft.py:596 - step 2291: {'train/loss': 0.017830967903137207, 'train/grad_norm': 0.3396199345588684, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:56:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:56:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:33] train_sft.py:596 - step 2292: {'train/loss': 0.008290797472000122, 'train/grad_norm': 0.20910604298114777, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:56:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:56:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:38] train_sft.py:596 - step 2293: {'train/loss': 0.014282256364822388, 'train/grad_norm': 0.2968178391456604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:56:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:56:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:42] train_sft.py:596 - step 2294: {'train/loss': 0.021743178367614746, 'train/grad_norm': 0.7063941955566406, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:56:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:56:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:47] train_sft.py:596 - step 2295: {'train/loss': 0.017145387828350067, 'train/grad_norm': 0.4883493185043335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:56:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:56:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:53] train_sft.py:596 - step 2296: {'train/loss': 0.02976168692111969, 'train/grad_norm': 0.474835604429245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 14:56:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 14:56:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:56:57] train_sft.py:596 - step 2297: {'train/loss': 0.016457799822092056, 'train/grad_norm': 0.46616294980049133, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:56:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:56:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:02] train_sft.py:596 - step 2298: {'train/loss': 0.018840432167053223, 'train/grad_norm': 0.39513981342315674, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:57:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:57:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:07] train_sft.py:596 - step 2299: {'train/loss': 0.01870860904455185, 'train/grad_norm': 0.5227155089378357, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 14:57:07] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:57:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:12] train_sft.py:596 - step 2300: {'train/loss': 0.01647556573152542, 'train/grad_norm': 0.5947439670562744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:57:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:57:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:17] train_sft.py:596 - step 2301: {'train/loss': 0.015196464955806732, 'train/grad_norm': 0.3247060179710388, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 14:57:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:57:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:22] train_sft.py:596 - step 2302: {'train/loss': 0.01518300175666809, 'train/grad_norm': 0.5257580280303955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 14:57:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:57:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:27] train_sft.py:596 - step 2303: {'train/loss': 0.020532667636871338, 'train/grad_norm': 0.5922462344169617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:57:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:57:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:31] train_sft.py:596 - step 2304: {'train/loss': 0.01839528977870941, 'train/grad_norm': 0.47245582938194275, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:57:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:57:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:36] train_sft.py:596 - step 2305: {'train/loss': 0.019247859716415405, 'train/grad_norm': 0.4704475402832031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 14:57:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:57:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:41] train_sft.py:596 - step 2306: {'train/loss': 0.03350058197975159, 'train/grad_norm': 1.2980164289474487, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 14:57:41] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:57:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:46] train_sft.py:596 - step 2307: {'train/loss': 0.014746267348527908, 'train/grad_norm': 0.3748847544193268, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:57:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:57:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:51] train_sft.py:596 - step 2308: {'train/loss': 0.013741359114646912, 'train/grad_norm': 0.6814762949943542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 14:57:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:57:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:57:55] train_sft.py:596 - step 2309: {'train/loss': 0.015019282698631287, 'train/grad_norm': 0.4922373294830322, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 14:57:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:57:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:00] train_sft.py:596 - step 2310: {'train/loss': 0.013096213340759277, 'train/grad_norm': 0.3055358827114105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:58:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:58:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:05] train_sft.py:596 - step 2311: {'train/loss': 0.018825560808181763, 'train/grad_norm': 0.572481632232666, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 14:58:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:58:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:09] train_sft.py:596 - step 2312: {'train/loss': 0.012214452028274536, 'train/grad_norm': 0.3833634853363037, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:58:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:58:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:14] train_sft.py:596 - step 2313: {'train/loss': 0.011766277253627777, 'train/grad_norm': 0.3508691191673279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 14:58:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:58:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:19] train_sft.py:596 - step 2314: {'train/loss': 0.015409395098686218, 'train/grad_norm': 0.39533939957618713, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:58:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:58:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:24] train_sft.py:596 - step 2315: {'train/loss': 0.02325814962387085, 'train/grad_norm': 0.4620695412158966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 14:58:24] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 14:58:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:29] train_sft.py:596 - step 2316: {'train/loss': 0.016285575926303864, 'train/grad_norm': 0.3143594264984131, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 14:58:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:58:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:34] train_sft.py:596 - step 2317: {'train/loss': 0.019657015800476074, 'train/grad_norm': 0.6081935167312622, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 14:58:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:58:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:38] train_sft.py:596 - step 2318: {'train/loss': 0.03022211790084839, 'train/grad_norm': 0.4677450358867645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:58:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:58:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:43] train_sft.py:596 - step 2319: {'train/loss': 0.018007807433605194, 'train/grad_norm': 0.4703176021575928, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 14:58:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:58:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:47] train_sft.py:596 - step 2320: {'train/loss': 0.023539230227470398, 'train/grad_norm': 0.4486922025680542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 14:58:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:58:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:52] train_sft.py:596 - step 2321: {'train/loss': 0.021748393774032593, 'train/grad_norm': 0.3816169500350952, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:58:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 14:58:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:58:57] train_sft.py:596 - step 2322: {'train/loss': 0.01667584478855133, 'train/grad_norm': 0.7667604684829712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:58:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:58:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:02] train_sft.py:596 - step 2323: {'train/loss': 0.017702177166938782, 'train/grad_norm': 0.5156148076057434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 14:59:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:59:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:06] train_sft.py:596 - step 2324: {'train/loss': 0.019583523273468018, 'train/grad_norm': 0.5511822700500488, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 14:59:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 14:59:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:11] train_sft.py:596 - step 2325: {'train/loss': 0.012195020914077759, 'train/grad_norm': 0.44671764969825745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 14:59:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:59:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:16] train_sft.py:596 - step 2326: {'train/loss': 0.020947128534317017, 'train/grad_norm': 0.7405857443809509, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 14:59:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 14:59:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:21] train_sft.py:596 - step 2327: {'train/loss': 0.01108086109161377, 'train/grad_norm': 0.329623818397522, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 14:59:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:59:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:25] train_sft.py:596 - step 2328: {'train/loss': 0.027704298496246338, 'train/grad_norm': 0.9804291725158691, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 14:59:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:59:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:30] train_sft.py:596 - step 2329: {'train/loss': 0.017659764736890793, 'train/grad_norm': 0.4074558913707733, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 14:59:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:59:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:35] train_sft.py:596 - step 2330: {'train/loss': 0.017298713326454163, 'train/grad_norm': 0.5847908854484558, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 14:59:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:59:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:40] train_sft.py:596 - step 2331: {'train/loss': 0.023093581199645996, 'train/grad_norm': 0.5953862071037292, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 14:59:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:59:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:45] train_sft.py:596 - step 2332: {'train/loss': 0.018295004963874817, 'train/grad_norm': 0.5953618288040161, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 14:59:45] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 14:59:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:49] train_sft.py:596 - step 2333: {'train/loss': 0.01932026445865631, 'train/grad_norm': 0.8448562026023865, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 14:59:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 14:59:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:54] train_sft.py:596 - step 2334: {'train/loss': 0.028642401099205017, 'train/grad_norm': 0.7619761228561401, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 14:59:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:59:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 14:59:59] train_sft.py:596 - step 2335: {'train/loss': 0.025331735610961914, 'train/grad_norm': 0.6507570147514343, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 14:59:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 14:59:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:04] train_sft.py:596 - step 2336: {'train/loss': 0.014339573681354523, 'train/grad_norm': 0.5636577606201172, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:00:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:00:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:09] train_sft.py:596 - step 2337: {'train/loss': 0.02116616815328598, 'train/grad_norm': 0.3802088797092438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 15:00:09] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:00:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:14] train_sft.py:596 - step 2338: {'train/loss': 0.022361531853675842, 'train/grad_norm': 0.4939841032028198, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:00:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:00:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:19] train_sft.py:596 - step 2339: {'train/loss': 0.018001794815063477, 'train/grad_norm': 0.46538326144218445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 15:00:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:00:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:23] train_sft.py:596 - step 2340: {'train/loss': 0.023094775155186653, 'train/grad_norm': 0.7414993643760681, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 15:00:24] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:00:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:28] train_sft.py:596 - step 2341: {'train/loss': 0.022680193185806274, 'train/grad_norm': 0.7154261469841003, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:00:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:00:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:33] train_sft.py:596 - step 2342: {'train/loss': 0.014831013977527618, 'train/grad_norm': 0.47595807909965515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 15:00:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:00:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:38] train_sft.py:596 - step 2343: {'train/loss': 0.05617287755012512, 'train/grad_norm': 0.6106560826301575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:00:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:00:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:43] train_sft.py:596 - step 2344: {'train/loss': 0.013579975813627243, 'train/grad_norm': 0.639850378036499, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 15:00:43] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:00:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:48] train_sft.py:596 - step 2345: {'train/loss': 0.021219730377197266, 'train/grad_norm': 0.5781327486038208, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:00:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:00:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:52] train_sft.py:596 - step 2346: {'train/loss': 0.02173846960067749, 'train/grad_norm': 0.4431568682193756, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:00:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:00:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:00:57] train_sft.py:596 - step 2347: {'train/loss': 0.014627218246459961, 'train/grad_norm': 0.35004162788391113, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:00:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:00:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:02] train_sft.py:596 - step 2348: {'train/loss': 0.023135490715503693, 'train/grad_norm': 0.559525728225708, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 15:01:02] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:01:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:07] train_sft.py:596 - step 2349: {'train/loss': 0.02034933865070343, 'train/grad_norm': 0.47501614689826965, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:01:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:01:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:12] train_sft.py:596 - step 2350: {'train/loss': 0.024560824036598206, 'train/grad_norm': 0.4736432135105133, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 15:01:12] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:01:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:17] train_sft.py:596 - step 2351: {'train/loss': 0.012935491278767586, 'train/grad_norm': 0.27210110425949097, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:01:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:01:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:22] train_sft.py:596 - step 2352: {'train/loss': 0.016581758856773376, 'train/grad_norm': 0.4999494254589081, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 15:01:22] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:01:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:27] train_sft.py:596 - step 2353: {'train/loss': 0.025922387838363647, 'train/grad_norm': 0.5440151691436768, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:01:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:01:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:32] train_sft.py:596 - step 2354: {'train/loss': 0.023951604962348938, 'train/grad_norm': 0.5933372378349304, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:01:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:01:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:36] train_sft.py:596 - step 2355: {'train/loss': 0.02810007333755493, 'train/grad_norm': 0.6484206318855286, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:01:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:01:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:41] train_sft.py:596 - step 2356: {'train/loss': 0.017194092273712158, 'train/grad_norm': 0.38031187653541565, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:01:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:01:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:46] train_sft.py:596 - step 2357: {'train/loss': 0.028040647506713867, 'train/grad_norm': 0.7436509132385254, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:01:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:01:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:51] train_sft.py:596 - step 2358: {'train/loss': 0.01875080168247223, 'train/grad_norm': 0.5176032781600952, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:01:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:01:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:01:56] train_sft.py:596 - step 2359: {'train/loss': 0.01788736879825592, 'train/grad_norm': 0.8380378484725952, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:01:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:01:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:01] train_sft.py:596 - step 2360: {'train/loss': 0.01567197497934103, 'train/grad_norm': 0.5749398469924927, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 15:02:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:02:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:05] train_sft.py:596 - step 2361: {'train/loss': 0.020159408450126648, 'train/grad_norm': 0.3751761019229889, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:02:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:02:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:10] train_sft.py:596 - step 2362: {'train/loss': 0.02443978190422058, 'train/grad_norm': 0.9716268181800842, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 15:02:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:02:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:16] train_sft.py:596 - step 2363: {'train/loss': 0.027907371520996094, 'train/grad_norm': 0.5636080503463745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 15:02:16] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:02:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:20] train_sft.py:596 - step 2364: {'train/loss': 0.01573432981967926, 'train/grad_norm': 0.35896220803260803, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:02:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:02:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:26] train_sft.py:596 - step 2365: {'train/loss': 0.018042564392089844, 'train/grad_norm': 0.5352466106414795, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 15:02:26] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:02:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:30] train_sft.py:596 - step 2366: {'train/loss': 0.02075064182281494, 'train/grad_norm': 0.6514971256256104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:02:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:02:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:36] train_sft.py:596 - step 2367: {'train/loss': 0.01515170931816101, 'train/grad_norm': 0.37911126017570496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 15:02:36] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:02:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:40] train_sft.py:596 - step 2368: {'train/loss': 0.02248305082321167, 'train/grad_norm': 0.4428466558456421, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:02:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:02:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:45] train_sft.py:596 - step 2369: {'train/loss': 0.019977986812591553, 'train/grad_norm': 0.5899741649627686, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:02:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:02:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:50] train_sft.py:596 - step 2370: {'train/loss': 0.013384222984313965, 'train/grad_norm': 0.3165062963962555, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:02:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:02:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:55] train_sft.py:596 - step 2371: {'train/loss': 0.017025500535964966, 'train/grad_norm': 0.3562230169773102, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:02:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:02:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:02:59] train_sft.py:596 - step 2372: {'train/loss': 0.013627320528030396, 'train/grad_norm': 0.37886154651641846, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:02:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:02:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:04] train_sft.py:596 - step 2373: {'train/loss': 0.011006414890289307, 'train/grad_norm': 0.2878352403640747, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 15:03:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:03:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:09] train_sft.py:596 - step 2374: {'train/loss': 0.019054055213928223, 'train/grad_norm': 0.44633230566978455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 15:03:09] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:03:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:14] train_sft.py:596 - step 2375: {'train/loss': 0.012452200055122375, 'train/grad_norm': 0.40684637427330017, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:03:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:03:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:19] train_sft.py:596 - step 2376: {'train/loss': 0.020594239234924316, 'train/grad_norm': 0.5644717812538147, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 15:03:19] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:03:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:24] train_sft.py:596 - step 2377: {'train/loss': 0.022931016981601715, 'train/grad_norm': 0.4668927490711212, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:03:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:03:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:28] train_sft.py:596 - step 2378: {'train/loss': 0.014219284057617188, 'train/grad_norm': 0.38728442788124084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:03:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:03:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:33] train_sft.py:596 - step 2379: {'train/loss': 0.023318976163864136, 'train/grad_norm': 0.46209776401519775, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:03:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:03:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:38] train_sft.py:596 - step 2380: {'train/loss': 0.016543090343475342, 'train/grad_norm': 0.3701251745223999, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 15:03:38] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:03:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:42] train_sft.py:596 - step 2381: {'train/loss': 0.02108946442604065, 'train/grad_norm': 0.8569901585578918, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:03:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:03:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:47] train_sft.py:596 - step 2382: {'train/loss': 0.02155984938144684, 'train/grad_norm': 0.714942455291748, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:03:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:03:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:52] train_sft.py:596 - step 2383: {'train/loss': 0.03376999497413635, 'train/grad_norm': 0.614460825920105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:03:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:03:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:03:57] train_sft.py:596 - step 2384: {'train/loss': 0.016874291002750397, 'train/grad_norm': 0.5280268788337708, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:03:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:03:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:02] train_sft.py:596 - step 2385: {'train/loss': 0.016148880124092102, 'train/grad_norm': 0.4909135699272156, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:04:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:04:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:06] train_sft.py:596 - step 2386: {'train/loss': 0.016960740089416504, 'train/grad_norm': 0.4797157943248749, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:04:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:04:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:11] train_sft.py:596 - step 2387: {'train/loss': 0.026109978556632996, 'train/grad_norm': 0.7187447547912598, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:04:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:04:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:15] train_sft.py:596 - step 2388: {'train/loss': 0.020501255989074707, 'train/grad_norm': 0.7235702276229858, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:04:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:04:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:20] train_sft.py:596 - step 2389: {'train/loss': 0.02610616385936737, 'train/grad_norm': 0.6669389605522156, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:04:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:04:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:26] train_sft.py:596 - step 2390: {'train/loss': 0.01839512586593628, 'train/grad_norm': 0.4737184941768646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 15:04:26] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:04:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:31] train_sft.py:596 - step 2391: {'train/loss': 0.01844097673892975, 'train/grad_norm': 0.558576226234436, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 15:04:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:04:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:35] train_sft.py:596 - step 2392: {'train/loss': 0.01692149043083191, 'train/grad_norm': 0.5203700065612793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:04:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:04:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:40] train_sft.py:596 - step 2393: {'train/loss': 0.015437051653862, 'train/grad_norm': 0.4760049879550934, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:04:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:04:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:45] train_sft.py:596 - step 2394: {'train/loss': 0.01653391122817993, 'train/grad_norm': 0.42313671112060547, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 15:04:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:04:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:50] train_sft.py:596 - step 2395: {'train/loss': 0.014509856700897217, 'train/grad_norm': 0.42336907982826233, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:04:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:04:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:55] train_sft.py:596 - step 2396: {'train/loss': 0.017639808356761932, 'train/grad_norm': 0.4653855264186859, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:04:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:04:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:04:59] train_sft.py:596 - step 2397: {'train/loss': 0.024608463048934937, 'train/grad_norm': 0.7535579204559326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:04:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:04] train_sft.py:596 - step 2398: {'train/loss': 0.01639533042907715, 'train/grad_norm': 0.4652959704399109, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:05:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:09] train_sft.py:596 - step 2399: {'train/loss': 0.023084312677383423, 'train/grad_norm': 0.6401163935661316, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:05:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:05:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:13] train_sft.py:596 - step 2400: {'train/loss': 0.016137588769197464, 'train/grad_norm': 0.601768434047699, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 15:05:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:05:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:18] train_sft.py:596 - step 2401: {'train/loss': 0.02003072202205658, 'train/grad_norm': 0.36766523122787476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:05:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:05:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:23] train_sft.py:596 - step 2402: {'train/loss': 0.015499666333198547, 'train/grad_norm': 0.4158487915992737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:05:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:05:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:28] train_sft.py:596 - step 2403: {'train/loss': 0.020801253616809845, 'train/grad_norm': 0.45960360765457153, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:05:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:05:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:33] train_sft.py:596 - step 2404: {'train/loss': 0.02056431770324707, 'train/grad_norm': 0.34203973412513733, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:05:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:05:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:38] train_sft.py:596 - step 2405: {'train/loss': 0.022346943616867065, 'train/grad_norm': 0.6139748692512512, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:05:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:42] train_sft.py:596 - step 2406: {'train/loss': 0.024767667055130005, 'train/grad_norm': 0.5435664653778076, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:05:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:47] train_sft.py:596 - step 2407: {'train/loss': 0.01658642292022705, 'train/grad_norm': 0.33539626002311707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:05:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:52] train_sft.py:596 - step 2408: {'train/loss': 0.021069586277008057, 'train/grad_norm': 0.46543529629707336, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:05:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:05:56] train_sft.py:596 - step 2409: {'train/loss': 0.02349616214632988, 'train/grad_norm': 0.5098717212677002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:05:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:05:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:01] train_sft.py:596 - step 2410: {'train/loss': 0.02174226939678192, 'train/grad_norm': 0.6376996040344238, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:06:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:06:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:06] train_sft.py:596 - step 2411: {'train/loss': 0.013327546417713165, 'train/grad_norm': 0.575745701789856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:06:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:06:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:10] train_sft.py:596 - step 2412: {'train/loss': 0.016063259914517403, 'train/grad_norm': 0.36248546838760376, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:06:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:06:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:15] train_sft.py:596 - step 2413: {'train/loss': 0.01845686137676239, 'train/grad_norm': 0.556143045425415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:06:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:06:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:20] train_sft.py:596 - step 2414: {'train/loss': 0.02614903450012207, 'train/grad_norm': 0.7069481611251831, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 15:06:20] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:06:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:25] train_sft.py:596 - step 2415: {'train/loss': 0.012174958363175392, 'train/grad_norm': 0.30377236008644104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:06:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:06:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:30] train_sft.py:596 - step 2416: {'train/loss': 0.02130652219057083, 'train/grad_norm': 0.5794990658760071, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:06:30] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:06:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:34] train_sft.py:596 - step 2417: {'train/loss': 0.024982810020446777, 'train/grad_norm': 0.632016658782959, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:06:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:06:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:39] train_sft.py:596 - step 2418: {'train/loss': 0.018174849450588226, 'train/grad_norm': 0.4384274482727051, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:06:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:06:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:44] train_sft.py:596 - step 2419: {'train/loss': 0.026967406272888184, 'train/grad_norm': 0.8485703468322754, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:06:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:06:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:48] train_sft.py:596 - step 2420: {'train/loss': 0.02069389820098877, 'train/grad_norm': 0.4305773079395294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:06:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:06:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:53] train_sft.py:596 - step 2421: {'train/loss': 0.020288825035095215, 'train/grad_norm': 0.3121848404407501, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:06:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:06:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:06:58] train_sft.py:596 - step 2422: {'train/loss': 0.0216333270072937, 'train/grad_norm': 0.6463494300842285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:06:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:06:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:03] train_sft.py:596 - step 2423: {'train/loss': 0.019864436239004135, 'train/grad_norm': 0.5028690695762634, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:07:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:07:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:08] train_sft.py:596 - step 2424: {'train/loss': 0.014801651239395142, 'train/grad_norm': 0.29931357502937317, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:07:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:07:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:12] train_sft.py:596 - step 2425: {'train/loss': 0.01471257209777832, 'train/grad_norm': 0.3556799292564392, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:07:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:07:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:17] train_sft.py:596 - step 2426: {'train/loss': 0.02175271511077881, 'train/grad_norm': 0.42552730441093445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:07:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:07:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:21] train_sft.py:596 - step 2427: {'train/loss': 0.024424154311418533, 'train/grad_norm': 0.45048463344573975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:07:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:07:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:26] train_sft.py:596 - step 2428: {'train/loss': 0.02242875099182129, 'train/grad_norm': 0.8606340289115906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:07:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:07:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:31] train_sft.py:596 - step 2429: {'train/loss': 0.01788042113184929, 'train/grad_norm': 0.5520413517951965, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:07:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:07:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:36] train_sft.py:596 - step 2430: {'train/loss': 0.018328040838241577, 'train/grad_norm': 0.6092248558998108, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:07:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:07:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:41] train_sft.py:596 - step 2431: {'train/loss': 0.023114264011383057, 'train/grad_norm': 0.65899658203125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:07:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:07:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:45] train_sft.py:596 - step 2432: {'train/loss': 0.01834210753440857, 'train/grad_norm': 0.3761257529258728, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:07:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:07:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:50] train_sft.py:596 - step 2433: {'train/loss': 0.02560436725616455, 'train/grad_norm': 0.5742244124412537, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:07:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:07:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:54] train_sft.py:596 - step 2434: {'train/loss': 0.011949002742767334, 'train/grad_norm': 0.43387678265571594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:07:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:07:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:07:59] train_sft.py:596 - step 2435: {'train/loss': 0.020254358649253845, 'train/grad_norm': 0.40023288130760193, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:07:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:07:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:04] train_sft.py:596 - step 2436: {'train/loss': 0.020443350076675415, 'train/grad_norm': 0.5882992148399353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:08:04] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:08:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:09] train_sft.py:596 - step 2437: {'train/loss': 0.020527705550193787, 'train/grad_norm': 0.39788761734962463, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:08:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:08:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:14] train_sft.py:596 - step 2438: {'train/loss': 0.01678144931793213, 'train/grad_norm': 0.2860478460788727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:08:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:08:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:18] train_sft.py:596 - step 2439: {'train/loss': 0.01937088370323181, 'train/grad_norm': 0.5537464618682861, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:08:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:08:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:23] train_sft.py:596 - step 2440: {'train/loss': 0.016564306803047657, 'train/grad_norm': 0.47834283113479614, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:08:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:08:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:27] train_sft.py:596 - step 2441: {'train/loss': 0.016833245754241943, 'train/grad_norm': 0.3993185758590698, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:08:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:08:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:32] train_sft.py:596 - step 2442: {'train/loss': 0.0230446457862854, 'train/grad_norm': 0.5497530698776245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:08:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:08:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:37] train_sft.py:596 - step 2443: {'train/loss': 0.02683928608894348, 'train/grad_norm': 0.5542618036270142, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:08:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:08:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:42] train_sft.py:596 - step 2444: {'train/loss': 0.015713155269622803, 'train/grad_norm': 0.3561721742153168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:08:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:08:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:46] train_sft.py:596 - step 2445: {'train/loss': 0.017685003578662872, 'train/grad_norm': 0.5419386029243469, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:08:46] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:08:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:51] train_sft.py:596 - step 2446: {'train/loss': 0.025580763816833496, 'train/grad_norm': 0.653670608997345, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:08:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:08:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:08:56] train_sft.py:596 - step 2447: {'train/loss': 0.03224819619208574, 'train/grad_norm': 0.6300525069236755, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:08:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:08:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:01] train_sft.py:596 - step 2448: {'train/loss': 0.027290940284729004, 'train/grad_norm': 0.6045021414756775, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:09:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:09:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:05] train_sft.py:596 - step 2449: {'train/loss': 0.0154104083776474, 'train/grad_norm': 0.42963603138923645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:09:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:09:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:10] train_sft.py:596 - step 2450: {'train/loss': 0.021210506558418274, 'train/grad_norm': 0.45231392979621887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:09:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:09:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:15] train_sft.py:596 - step 2451: {'train/loss': 0.015177667140960693, 'train/grad_norm': 0.4245062470436096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:09:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:09:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:19] train_sft.py:596 - step 2452: {'train/loss': 0.022616833448410034, 'train/grad_norm': 0.5019595623016357, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:09:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:09:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:24] train_sft.py:596 - step 2453: {'train/loss': 0.01619553565979004, 'train/grad_norm': 0.24238045513629913, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:09:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:09:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:28] train_sft.py:596 - step 2454: {'train/loss': 0.017150014638900757, 'train/grad_norm': 0.501263439655304, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:09:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:09:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:33] train_sft.py:596 - step 2455: {'train/loss': 0.023682198487222195, 'train/grad_norm': 0.41888320446014404, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 15:09:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:09:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:38] train_sft.py:596 - step 2456: {'train/loss': 0.02233576774597168, 'train/grad_norm': 0.5458429455757141, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:09:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:09:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:43] train_sft.py:596 - step 2457: {'train/loss': 0.021477073431015015, 'train/grad_norm': 0.5778571367263794, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:09:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:09:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:47] train_sft.py:596 - step 2458: {'train/loss': 0.02288353443145752, 'train/grad_norm': 0.6041104197502136, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:09:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:09:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:52] train_sft.py:596 - step 2459: {'train/loss': 0.02072998881340027, 'train/grad_norm': 0.3949325978755951, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:09:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:09:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:09:57] train_sft.py:596 - step 2460: {'train/loss': 0.02023562788963318, 'train/grad_norm': 0.6124125123023987, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:09:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:09:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:02] train_sft.py:596 - step 2461: {'train/loss': 0.01697429269552231, 'train/grad_norm': 0.4265972971916199, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:10:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:10:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:07] train_sft.py:596 - step 2462: {'train/loss': 0.023732081055641174, 'train/grad_norm': 0.2747957408428192, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:10:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:10:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:11] train_sft.py:596 - step 2463: {'train/loss': 0.019392505288124084, 'train/grad_norm': 0.3578091561794281, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 15:10:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:10:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:16] train_sft.py:596 - step 2464: {'train/loss': 0.011301636695861816, 'train/grad_norm': 0.32406625151634216, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:10:16] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:10:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:21] train_sft.py:596 - step 2465: {'train/loss': 0.02691960334777832, 'train/grad_norm': 0.5104823708534241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:10:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:10:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:25] train_sft.py:596 - step 2466: {'train/loss': 0.023230701684951782, 'train/grad_norm': 0.5149843692779541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:10:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:10:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:30] train_sft.py:596 - step 2467: {'train/loss': 0.015731528401374817, 'train/grad_norm': 0.43827781081199646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:10:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:10:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:34] train_sft.py:596 - step 2468: {'train/loss': 0.026037178933620453, 'train/grad_norm': 0.5702704191207886, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:10:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:10:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:39] train_sft.py:596 - step 2469: {'train/loss': 0.02233414351940155, 'train/grad_norm': 0.5748853087425232, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:10:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:10:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:44] train_sft.py:596 - step 2470: {'train/loss': 0.022228330373764038, 'train/grad_norm': 0.6658951640129089, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:10:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:10:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:49] train_sft.py:596 - step 2471: {'train/loss': 0.02303314208984375, 'train/grad_norm': 0.682849645614624, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:10:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:10:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:53] train_sft.py:596 - step 2472: {'train/loss': 0.021373987197875977, 'train/grad_norm': 0.5124070048332214, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:10:53] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:10:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:10:58] train_sft.py:596 - step 2473: {'train/loss': 0.018992453813552856, 'train/grad_norm': 0.5721275210380554, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:10:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:10:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:03] train_sft.py:596 - step 2474: {'train/loss': 0.024391725659370422, 'train/grad_norm': 0.5738755464553833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:11:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:11:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:08] train_sft.py:596 - step 2475: {'train/loss': 0.0231478214263916, 'train/grad_norm': 0.8453653454780579, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:11:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:11:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:12] train_sft.py:596 - step 2476: {'train/loss': 0.025511503219604492, 'train/grad_norm': 0.534346342086792, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:11:12] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:11:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:17] train_sft.py:596 - step 2477: {'train/loss': 0.018703468143939972, 'train/grad_norm': 0.39989763498306274, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:11:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:11:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:22] train_sft.py:596 - step 2478: {'train/loss': 0.01414710283279419, 'train/grad_norm': 0.6811503767967224, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 15:11:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:11:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:27] train_sft.py:596 - step 2479: {'train/loss': 0.021948288194835186, 'train/grad_norm': 0.4655788838863373, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:11:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:11:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:32] train_sft.py:596 - step 2480: {'train/loss': 0.013772159814834595, 'train/grad_norm': 0.2809475362300873, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:11:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:11:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:37] train_sft.py:596 - step 2481: {'train/loss': 0.010026633739471436, 'train/grad_norm': 0.25960978865623474, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 15:11:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:11:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:41] train_sft.py:596 - step 2482: {'train/loss': 0.012670010328292847, 'train/grad_norm': 0.2847222089767456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:11:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:11:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:47] train_sft.py:596 - step 2483: {'train/loss': 0.020090825855731964, 'train/grad_norm': 0.46427223086357117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 15:11:47] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:11:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:51] train_sft.py:596 - step 2484: {'train/loss': 0.01177167147397995, 'train/grad_norm': 0.34724318981170654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:11:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:11:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:11:56] train_sft.py:596 - step 2485: {'train/loss': 0.013907089829444885, 'train/grad_norm': 0.550221860408783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:11:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:11:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:00] train_sft.py:596 - step 2486: {'train/loss': 0.025976166129112244, 'train/grad_norm': 0.5871529579162598, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:12:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:12:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:05] train_sft.py:596 - step 2487: {'train/loss': 0.018746167421340942, 'train/grad_norm': 0.5259388089179993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 15:12:05] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:12:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:10] train_sft.py:596 - step 2488: {'train/loss': 0.01719053089618683, 'train/grad_norm': 0.5745304822921753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:12:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:12:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:15] train_sft.py:596 - step 2489: {'train/loss': 0.01114875078201294, 'train/grad_norm': 0.4632861912250519, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:12:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:12:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:20] train_sft.py:596 - step 2490: {'train/loss': 0.013872392475605011, 'train/grad_norm': 0.3321782946586609, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:12:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:12:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:24] train_sft.py:596 - step 2491: {'train/loss': 0.019218608736991882, 'train/grad_norm': 0.7290934920310974, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:12:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:12:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:29] train_sft.py:596 - step 2492: {'train/loss': 0.01117154210805893, 'train/grad_norm': 0.3482050895690918, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:12:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:12:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:34] train_sft.py:596 - step 2493: {'train/loss': 0.01258552074432373, 'train/grad_norm': 0.43356969952583313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:12:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:12:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:39] train_sft.py:596 - step 2494: {'train/loss': 0.01587936282157898, 'train/grad_norm': 0.6987516283988953, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 15:12:39] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:12:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:43] train_sft.py:596 - step 2495: {'train/loss': 0.017390742897987366, 'train/grad_norm': 0.46466103196144104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:12:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:12:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:48] train_sft.py:596 - step 2496: {'train/loss': 0.009458422660827637, 'train/grad_norm': 0.2682143449783325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:12:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:12:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:53] train_sft.py:596 - step 2497: {'train/loss': 0.0139390230178833, 'train/grad_norm': 0.29568758606910706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:12:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:12:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:12:58] train_sft.py:596 - step 2498: {'train/loss': 0.015621364116668701, 'train/grad_norm': 0.4732085168361664, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:12:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:12:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:13:02] train_sft.py:596 - step 2499: {'train/loss': 0.020663287490606308, 'train/grad_norm': 0.42150384187698364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 15:13:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:01<02:42,  1.64s/it]
actor_val:   2%|▏         | 2/100 [00:03<02:48,  1.72s/it]
actor_val:   3%|▎         | 3/100 [00:04<02:39,  1.65s/it]
actor_val:   4%|▍         | 4/100 [00:06<02:35,  1.62s/it]
actor_val:   5%|▌         | 5/100 [00:08<02:32,  1.60s/it]
actor_val:   6%|▌         | 6/100 [00:09<02:29,  1.59s/it]
actor_val:   7%|▋         | 7/100 [00:11<02:27,  1.58s/it]
actor_val:   8%|▊         | 8/100 [00:12<02:24,  1.57s/it]
actor_val:   9%|▉         | 9/100 [00:14<02:29,  1.64s/it]
actor_val:  10%|█         | 10/100 [00:16<02:26,  1.63s/it]
actor_val:  11%|█         | 11/100 [00:18<02:30,  1.69s/it]
actor_val:  12%|█▏        | 12/100 [00:19<02:25,  1.65s/it]
actor_val:  13%|█▎        | 13/100 [00:21<02:27,  1.70s/it]
actor_val:  14%|█▍        | 14/100 [00:22<02:22,  1.66s/it]
actor_val:  15%|█▌        | 15/100 [00:24<02:18,  1.63s/it]
actor_val:  16%|█▌        | 16/100 [00:26<02:15,  1.61s/it]
actor_val:  17%|█▋        | 17/100 [00:27<02:12,  1.60s/it]
actor_val:  18%|█▊        | 18/100 [00:29<02:09,  1.58s/it]
actor_val:  19%|█▉        | 19/100 [00:30<02:06,  1.57s/it]
actor_val:  20%|██        | 20/100 [00:32<02:09,  1.62s/it]
actor_val:  21%|██        | 21/100 [00:34<02:07,  1.61s/it]
actor_val:  22%|██▏       | 22/100 [00:35<02:10,  1.68s/it]
actor_val:  23%|██▎       | 23/100 [00:37<02:11,  1.71s/it]
actor_val:  24%|██▍       | 24/100 [00:39<02:05,  1.65s/it]
actor_val:  25%|██▌       | 25/100 [00:40<02:01,  1.62s/it]
actor_val:  26%|██▌       | 26/100 [00:42<01:59,  1.61s/it]
actor_val:  27%|██▋       | 27/100 [00:43<01:57,  1.61s/it]
actor_val:  28%|██▊       | 28/100 [00:45<01:59,  1.66s/it]
actor_val:  29%|██▉       | 29/100 [00:47<01:55,  1.63s/it]
actor_val:  30%|███       | 30/100 [00:48<01:52,  1.61s/it]
actor_val:  31%|███       | 31/100 [00:50<01:55,  1.67s/it]
actor_val:  32%|███▏      | 32/100 [00:52<01:56,  1.71s/it]
actor_val:  33%|███▎      | 33/100 [00:54<01:57,  1.75s/it]
actor_val:  34%|███▍      | 34/100 [00:56<01:57,  1.77s/it]
actor_val:  35%|███▌      | 35/100 [00:57<01:56,  1.79s/it]
actor_val:  36%|███▌      | 36/100 [00:59<01:50,  1.73s/it]
actor_val:  37%|███▋      | 37/100 [01:01<01:45,  1.68s/it]
actor_val:  38%|███▊      | 38/100 [01:02<01:41,  1.64s/it]
actor_val:  39%|███▉      | 39/100 [01:04<01:39,  1.63s/it]
actor_val:  40%|████      | 40/100 [01:06<01:40,  1.68s/it]
actor_val:  41%|████      | 41/100 [01:07<01:36,  1.63s/it]
actor_val:  42%|████▏     | 42/100 [01:09<01:38,  1.70s/it]
actor_val:  43%|████▎     | 43/100 [01:11<01:34,  1.66s/it]
actor_val:  44%|████▍     | 44/100 [01:12<01:31,  1.63s/it]
actor_val:  45%|████▌     | 45/100 [01:14<01:32,  1.68s/it]
actor_val:  46%|████▌     | 46/100 [01:15<01:28,  1.65s/it]
actor_val:  47%|████▋     | 47/100 [01:17<01:29,  1.68s/it]
actor_val:  48%|████▊     | 48/100 [01:19<01:30,  1.73s/it]
actor_val:  49%|████▉     | 49/100 [01:21<01:28,  1.74s/it]
actor_val:  50%|█████     | 50/100 [01:22<01:24,  1.69s/it]
actor_val:  51%|█████     | 51/100 [01:24<01:24,  1.72s/it]
actor_val:  52%|█████▏    | 52/100 [01:26<01:20,  1.67s/it]
actor_val:  53%|█████▎    | 53/100 [01:27<01:16,  1.63s/it]
actor_val:  54%|█████▍    | 54/100 [01:29<01:13,  1.61s/it]
actor_val:  55%|█████▌    | 55/100 [01:30<01:11,  1.59s/it]
actor_val:  56%|█████▌    | 56/100 [01:32<01:09,  1.58s/it]
actor_val:  57%|█████▋    | 57/100 [01:34<01:11,  1.66s/it]
actor_val:  58%|█████▊    | 58/100 [01:35<01:09,  1.65s/it]
actor_val:  59%|█████▉    | 59/100 [01:37<01:10,  1.72s/it]
actor_val:  60%|██████    | 60/100 [01:39<01:09,  1.74s/it]
actor_val:  61%|██████    | 61/100 [01:41<01:05,  1.68s/it]
actor_val:  62%|██████▏   | 62/100 [01:42<01:03,  1.66s/it]
actor_val:  63%|██████▎   | 63/100 [01:44<01:03,  1.72s/it]
actor_val:  64%|██████▍   | 64/100 [01:46<01:02,  1.74s/it]
actor_val:  65%|██████▌   | 65/100 [01:48<01:02,  1.79s/it]
actor_val:  66%|██████▌   | 66/100 [01:50<01:00,  1.78s/it]
actor_val:  67%|██████▋   | 67/100 [01:51<00:56,  1.71s/it]
actor_val:  68%|██████▊   | 68/100 [01:53<00:55,  1.74s/it]
actor_val:  69%|██████▉   | 69/100 [01:55<00:52,  1.70s/it]
actor_val:  70%|███████   | 70/100 [01:56<00:51,  1.72s/it]
actor_val:  71%|███████   | 71/100 [01:58<00:49,  1.69s/it]
actor_val:  72%|███████▏  | 72/100 [01:59<00:46,  1.64s/it]
actor_val:  73%|███████▎  | 73/100 [02:01<00:45,  1.70s/it]
actor_val:  74%|███████▍  | 74/100 [02:03<00:44,  1.72s/it]
actor_val:  75%|███████▌  | 75/100 [02:05<00:41,  1.67s/it]
actor_val:  76%|███████▌  | 76/100 [02:06<00:41,  1.72s/it]
actor_val:  77%|███████▋  | 77/100 [02:08<00:40,  1.74s/it]
actor_val:  78%|███████▊  | 78/100 [02:10<00:36,  1.68s/it]
actor_val:  79%|███████▉  | 79/100 [02:11<00:34,  1.65s/it]
actor_val:  80%|████████  | 80/100 [02:13<00:32,  1.62s/it]
actor_val:  81%|████████  | 81/100 [02:14<00:30,  1.60s/it]
actor_val:  82%|████████▏ | 82/100 [02:16<00:28,  1.58s/it]
actor_val:  83%|████████▎ | 83/100 [02:18<00:27,  1.64s/it]
actor_val:  84%|████████▍ | 84/100 [02:19<00:25,  1.62s/it]
actor_val:  85%|████████▌ | 85/100 [02:21<00:23,  1.60s/it]
actor_val:  86%|████████▌ | 86/100 [02:23<00:23,  1.66s/it]
actor_val:  87%|████████▋ | 87/100 [02:25<00:22,  1.71s/it]
actor_val:  88%|████████▊ | 88/100 [02:26<00:21,  1.75s/it]
actor_val:  89%|████████▉ | 89/100 [02:28<00:18,  1.70s/it]
actor_val:  90%|█████████ | 90/100 [02:29<00:16,  1.66s/it]
actor_val:  91%|█████████ | 91/100 [02:31<00:14,  1.63s/it]
actor_val:  92%|█████████▏| 92/100 [02:33<00:12,  1.60s/it]
actor_val:  93%|█████████▎| 93/100 [02:35<00:11,  1.69s/it]
actor_val:  94%|█████████▍| 94/100 [02:36<00:10,  1.76s/it]
actor_val:  95%|█████████▌| 95/100 [02:38<00:08,  1.70s/it]
actor_val:  96%|█████████▌| 96/100 [02:40<00:06,  1.65s/it]
actor_val:  97%|█████████▋| 97/100 [02:41<00:04,  1.63s/it]
actor_val:  98%|█████████▊| 98/100 [02:43<00:03,  1.60s/it]
actor_val:  99%|█████████▉| 99/100 [02:44<00:01,  1.57s/it]
actor_val: 100%|██████████| 100/100 [02:46<00:00,  1.66s/it]
actor_val: 100%|██████████| 100/100 [02:46<00:00,  1.67s/it]
[2025-12-29 15:15:49] train_sft.py:658 - step 2500: {'val/loss': 0.450859375}
[2025-12-29 15:15:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:15:54] train_sft.py:596 - step 2500: {'train/loss': 0.01600472256541252, 'train/grad_norm': 0.5558240413665771, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:15:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:15:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:15:59] train_sft.py:596 - step 2501: {'train/loss': 0.011732548475265503, 'train/grad_norm': 0.3000476062297821, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.12s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.12s/it]
[2025-12-29 15:15:59] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 15:15:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:04] train_sft.py:596 - step 2502: {'train/loss': 0.013363376259803772, 'train/grad_norm': 0.3293408453464508, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:16:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:16:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:09] train_sft.py:596 - step 2503: {'train/loss': 0.018478401005268097, 'train/grad_norm': 0.6067838668823242, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:16:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:16:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:13] train_sft.py:596 - step 2504: {'train/loss': 0.011551737785339355, 'train/grad_norm': 0.4688417911529541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:16:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:16:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:18] train_sft.py:596 - step 2505: {'train/loss': 0.01704379916191101, 'train/grad_norm': 0.37145793437957764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:16:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:16:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:23] train_sft.py:596 - step 2506: {'train/loss': 0.013999074697494507, 'train/grad_norm': 0.46871158480644226, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 15:16:23] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:16:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:28] train_sft.py:596 - step 2507: {'train/loss': 0.019606083631515503, 'train/grad_norm': 0.5766263008117676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:16:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:16:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:32] train_sft.py:596 - step 2508: {'train/loss': 0.009345135651528835, 'train/grad_norm': 0.5145596861839294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:16:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:16:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:37] train_sft.py:596 - step 2509: {'train/loss': 0.016582898795604706, 'train/grad_norm': 0.3756721317768097, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:16:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:16:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:42] train_sft.py:596 - step 2510: {'train/loss': 0.010141700506210327, 'train/grad_norm': 0.5928410291671753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:16:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:16:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:47] train_sft.py:596 - step 2511: {'train/loss': 0.00954466313123703, 'train/grad_norm': 0.32429027557373047, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 15:16:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:16:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:51] train_sft.py:596 - step 2512: {'train/loss': 0.01276765763759613, 'train/grad_norm': 0.4184258282184601, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:16:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:16:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:16:56] train_sft.py:596 - step 2513: {'train/loss': 0.01036633551120758, 'train/grad_norm': 0.3024912178516388, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:16:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:16:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:01] train_sft.py:596 - step 2514: {'train/loss': 0.022666960954666138, 'train/grad_norm': 0.46784543991088867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:17:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:17:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:06] train_sft.py:596 - step 2515: {'train/loss': 0.01512901484966278, 'train/grad_norm': 0.6194490194320679, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.15s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.15s/it]
[2025-12-29 15:17:06] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 15:17:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:11] train_sft.py:596 - step 2516: {'train/loss': 0.016264110803604126, 'train/grad_norm': 0.40240663290023804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:17:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:17:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:16] train_sft.py:596 - step 2517: {'train/loss': 0.019329840317368507, 'train/grad_norm': 0.4001695215702057, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:17:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:17:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:21] train_sft.py:596 - step 2518: {'train/loss': 0.011648297309875488, 'train/grad_norm': 0.2186119705438614, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:17:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:17:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:25] train_sft.py:596 - step 2519: {'train/loss': 0.018735304474830627, 'train/grad_norm': 0.5936465263366699, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:17:25] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:17:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:30] train_sft.py:596 - step 2520: {'train/loss': 0.018047265708446503, 'train/grad_norm': 0.48168137669563293, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:17:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:17:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:35] train_sft.py:596 - step 2521: {'train/loss': 0.020185165107250214, 'train/grad_norm': 0.5105786919593811, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:17:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:17:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:40] train_sft.py:596 - step 2522: {'train/loss': 0.015484437346458435, 'train/grad_norm': 0.26861733198165894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:17:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:17:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:44] train_sft.py:596 - step 2523: {'train/loss': 0.016855984926223755, 'train/grad_norm': 0.4422833323478699, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:17:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:17:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:49] train_sft.py:596 - step 2524: {'train/loss': 0.020408228039741516, 'train/grad_norm': 0.5339232087135315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 15:17:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:17:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:54] train_sft.py:596 - step 2525: {'train/loss': 0.01837785914540291, 'train/grad_norm': 0.40556517243385315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:17:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:17:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:17:58] train_sft.py:596 - step 2526: {'train/loss': 0.02095458237454295, 'train/grad_norm': 0.5852790474891663, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:17:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:17:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:03] train_sft.py:596 - step 2527: {'train/loss': 0.01952725648880005, 'train/grad_norm': 0.6673255562782288, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:18:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:18:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:08] train_sft.py:596 - step 2528: {'train/loss': 0.02645745873451233, 'train/grad_norm': 0.7653301358222961, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:18:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:18:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:12] train_sft.py:596 - step 2529: {'train/loss': 0.007595419883728027, 'train/grad_norm': 0.24854281544685364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:18:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:18:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:17] train_sft.py:596 - step 2530: {'train/loss': 0.012593850493431091, 'train/grad_norm': 0.5334274172782898, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:18:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:18:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:22] train_sft.py:596 - step 2531: {'train/loss': 0.012000232934951782, 'train/grad_norm': 0.2619091272354126, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:18:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:18:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:26] train_sft.py:596 - step 2532: {'train/loss': 0.013473659753799438, 'train/grad_norm': 0.3022480905056, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:18:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:18:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:31] train_sft.py:596 - step 2533: {'train/loss': 0.0158054418861866, 'train/grad_norm': 0.3367372751235962, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:18:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:18:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:36] train_sft.py:596 - step 2534: {'train/loss': 0.018483757972717285, 'train/grad_norm': 0.5228554606437683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:18:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:18:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:40] train_sft.py:596 - step 2535: {'train/loss': 0.012652605772018433, 'train/grad_norm': 0.35953402519226074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:18:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:18:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:46] train_sft.py:596 - step 2536: {'train/loss': 0.017530053853988647, 'train/grad_norm': 0.5546983480453491, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 15:18:46] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:18:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:51] train_sft.py:596 - step 2537: {'train/loss': 0.019020259380340576, 'train/grad_norm': 0.44945526123046875, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 15:18:51] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:18:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:18:56] train_sft.py:596 - step 2538: {'train/loss': 0.01794912852346897, 'train/grad_norm': 0.36130791902542114, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 15:18:56] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:18:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:00] train_sft.py:596 - step 2539: {'train/loss': 0.014938771724700928, 'train/grad_norm': 0.26608020067214966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:19:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:19:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:05] train_sft.py:596 - step 2540: {'train/loss': 0.028119191527366638, 'train/grad_norm': 0.5581162571907043, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:19:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:19:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:10] train_sft.py:596 - step 2541: {'train/loss': 0.014308586716651917, 'train/grad_norm': 0.40655970573425293, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:19:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:19:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:15] train_sft.py:596 - step 2542: {'train/loss': 0.021057069301605225, 'train/grad_norm': 0.8902987837791443, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:19:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:19:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:19] train_sft.py:596 - step 2543: {'train/loss': 0.01667293906211853, 'train/grad_norm': 0.3186543583869934, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:19:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:19:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:24] train_sft.py:596 - step 2544: {'train/loss': 0.014036566019058228, 'train/grad_norm': 0.44061142206192017, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:19:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:19:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:29] train_sft.py:596 - step 2545: {'train/loss': 0.019659966230392456, 'train/grad_norm': 0.6318086385726929, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:19:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:19:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:34] train_sft.py:596 - step 2546: {'train/loss': 0.016733869910240173, 'train/grad_norm': 0.38541197776794434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:19:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:19:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:38] train_sft.py:596 - step 2547: {'train/loss': 0.013100504875183105, 'train/grad_norm': 0.4052581489086151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:19:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:19:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:43] train_sft.py:596 - step 2548: {'train/loss': 0.02641206979751587, 'train/grad_norm': 0.6707689166069031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:19:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:19:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:48] train_sft.py:596 - step 2549: {'train/loss': 0.02133484184741974, 'train/grad_norm': 0.3711602985858917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:19:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:19:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:52] train_sft.py:596 - step 2550: {'train/loss': 0.013129562139511108, 'train/grad_norm': 0.5463128089904785, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:19:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:19:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:19:57] train_sft.py:596 - step 2551: {'train/loss': 0.012930795550346375, 'train/grad_norm': 0.3085757791996002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 15:19:57] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:19:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:02] train_sft.py:596 - step 2552: {'train/loss': 0.015585601329803467, 'train/grad_norm': 0.5061787962913513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:20:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:20:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:07] train_sft.py:596 - step 2553: {'train/loss': 0.01703619956970215, 'train/grad_norm': 0.6742058992385864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 15:20:07] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:20:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:12] train_sft.py:596 - step 2554: {'train/loss': 0.01896136999130249, 'train/grad_norm': 0.7871975898742676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:20:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:20:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:16] train_sft.py:596 - step 2555: {'train/loss': 0.015500932931900024, 'train/grad_norm': 0.505301296710968, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:20:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:20:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:21] train_sft.py:596 - step 2556: {'train/loss': 0.011047452688217163, 'train/grad_norm': 0.33590248227119446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:20:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:20:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:26] train_sft.py:596 - step 2557: {'train/loss': 0.02094554901123047, 'train/grad_norm': 0.4050807058811188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:20:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:20:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:31] train_sft.py:596 - step 2558: {'train/loss': 0.01351076364517212, 'train/grad_norm': 0.4396381676197052, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:20:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:20:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:35] train_sft.py:596 - step 2559: {'train/loss': 0.01978319138288498, 'train/grad_norm': 0.6906650066375732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:20:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:20:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:40] train_sft.py:596 - step 2560: {'train/loss': 0.02238909900188446, 'train/grad_norm': 0.5387440323829651, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:20:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:20:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:45] train_sft.py:596 - step 2561: {'train/loss': 0.014444664120674133, 'train/grad_norm': 0.6778673529624939, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:20:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:20:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:49] train_sft.py:596 - step 2562: {'train/loss': 0.016022101044654846, 'train/grad_norm': 0.4350747764110565, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:20:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:20:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:54] train_sft.py:596 - step 2563: {'train/loss': 0.031654179096221924, 'train/grad_norm': 0.6806560754776001, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:20:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:20:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:20:59] train_sft.py:596 - step 2564: {'train/loss': 0.014609485864639282, 'train/grad_norm': 0.2554863691329956, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 15:20:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:20:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:04] train_sft.py:596 - step 2565: {'train/loss': 0.01500481367111206, 'train/grad_norm': 0.4269518256187439, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:21:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:21:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:08] train_sft.py:596 - step 2566: {'train/loss': 0.012905701994895935, 'train/grad_norm': 0.31081706285476685, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:21:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:21:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:13] train_sft.py:596 - step 2567: {'train/loss': 0.018680989742279053, 'train/grad_norm': 0.8348078727722168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:21:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:21:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:18] train_sft.py:596 - step 2568: {'train/loss': 0.013272374868392944, 'train/grad_norm': 0.5209010243415833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 15:21:18] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:21:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:22] train_sft.py:596 - step 2569: {'train/loss': 0.009243011474609375, 'train/grad_norm': 0.3325175940990448, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:21:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:21:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:27] train_sft.py:596 - step 2570: {'train/loss': 0.015498340129852295, 'train/grad_norm': 0.6191273927688599, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:21:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:21:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:31] train_sft.py:596 - step 2571: {'train/loss': 0.015566706657409668, 'train/grad_norm': 0.31169313192367554, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:21:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:21:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:36] train_sft.py:596 - step 2572: {'train/loss': 0.016313277184963226, 'train/grad_norm': 0.49615129828453064, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:21:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:21:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:41] train_sft.py:596 - step 2573: {'train/loss': 0.011216051876544952, 'train/grad_norm': 0.33138948678970337, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:21:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:21:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:46] train_sft.py:596 - step 2574: {'train/loss': 0.017805784940719604, 'train/grad_norm': 0.6110475063323975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:21:46] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:21:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:51] train_sft.py:596 - step 2575: {'train/loss': 0.01772114634513855, 'train/grad_norm': 0.4076564311981201, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:21:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:21:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:21:55] train_sft.py:596 - step 2576: {'train/loss': 0.016144782304763794, 'train/grad_norm': 0.3436841368675232, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:21:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:21:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:00] train_sft.py:596 - step 2577: {'train/loss': 0.017770826816558838, 'train/grad_norm': 0.38364556431770325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:22:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:22:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:05] train_sft.py:596 - step 2578: {'train/loss': 0.018457606434822083, 'train/grad_norm': 0.675822913646698, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:22:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:22:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:10] train_sft.py:596 - step 2579: {'train/loss': 0.024816885590553284, 'train/grad_norm': 0.5694804191589355, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:22:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:22:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:14] train_sft.py:596 - step 2580: {'train/loss': 0.018290072679519653, 'train/grad_norm': 0.325134813785553, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:22:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:22:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:19] train_sft.py:596 - step 2581: {'train/loss': 0.022003665566444397, 'train/grad_norm': 0.5311702489852905, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:22:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:22:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:24] train_sft.py:596 - step 2582: {'train/loss': 0.024959921836853027, 'train/grad_norm': 0.7452519536018372, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:22:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:22:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:28] train_sft.py:596 - step 2583: {'train/loss': 0.017234086990356445, 'train/grad_norm': 0.4650006890296936, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:22:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:22:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:33] train_sft.py:596 - step 2584: {'train/loss': 0.011638099327683449, 'train/grad_norm': 0.2847049832344055, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:22:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:22:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:38] train_sft.py:596 - step 2585: {'train/loss': 0.015312179923057556, 'train/grad_norm': 0.429096519947052, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:22:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:22:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:43] train_sft.py:596 - step 2586: {'train/loss': 0.01857057213783264, 'train/grad_norm': 0.537248432636261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:22:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:22:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:48] train_sft.py:596 - step 2587: {'train/loss': 0.01483374834060669, 'train/grad_norm': 0.48364368081092834, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:22:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:22:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:52] train_sft.py:596 - step 2588: {'train/loss': 0.011292517185211182, 'train/grad_norm': 0.3024991750717163, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:22:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:22:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:22:57] train_sft.py:596 - step 2589: {'train/loss': 0.013253927230834961, 'train/grad_norm': 0.4748970568180084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:22:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:22:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:01] train_sft.py:596 - step 2590: {'train/loss': 0.01726353168487549, 'train/grad_norm': 0.40308311581611633, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:23:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:06] train_sft.py:596 - step 2591: {'train/loss': 0.019043803215026855, 'train/grad_norm': 0.7263171076774597, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:23:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:23:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:11] train_sft.py:596 - step 2592: {'train/loss': 0.016076579689979553, 'train/grad_norm': 0.3831082880496979, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 15:23:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:23:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:15] train_sft.py:596 - step 2593: {'train/loss': 0.02212204784154892, 'train/grad_norm': 0.6860853433609009, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:23:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:20] train_sft.py:596 - step 2594: {'train/loss': 0.016669437289237976, 'train/grad_norm': 0.5363158583641052, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:23:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:23:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:25] train_sft.py:596 - step 2595: {'train/loss': 0.01827050745487213, 'train/grad_norm': 0.37751349806785583, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:23:25] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:23:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:30] train_sft.py:596 - step 2596: {'train/loss': 0.022911667823791504, 'train/grad_norm': 0.795311689376831, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:23:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:34] train_sft.py:596 - step 2597: {'train/loss': 0.01518237590789795, 'train/grad_norm': 0.5798554420471191, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:23:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:39] train_sft.py:596 - step 2598: {'train/loss': 0.015313506126403809, 'train/grad_norm': 0.595778226852417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:23:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:44] train_sft.py:596 - step 2599: {'train/loss': 0.011936753988265991, 'train/grad_norm': 0.43836650252342224, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:23:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:48] train_sft.py:596 - step 2600: {'train/loss': 0.020119428634643555, 'train/grad_norm': 0.8056772351264954, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:23:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:23:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:53] train_sft.py:596 - step 2601: {'train/loss': 0.019269511103630066, 'train/grad_norm': 0.42535606026649475, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:23:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:23:58] train_sft.py:596 - step 2602: {'train/loss': 0.023633204400539398, 'train/grad_norm': 0.5639228224754333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:23:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:23:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:02] train_sft.py:596 - step 2603: {'train/loss': 0.021378636360168457, 'train/grad_norm': 0.47443443536758423, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:24:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:24:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:07] train_sft.py:596 - step 2604: {'train/loss': 0.011756733059883118, 'train/grad_norm': 0.37879127264022827, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:24:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:24:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:12] train_sft.py:596 - step 2605: {'train/loss': 0.013264179229736328, 'train/grad_norm': 0.39133739471435547, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:24:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:24:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:16] train_sft.py:596 - step 2606: {'train/loss': 0.0162067711353302, 'train/grad_norm': 0.47945636510849, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:24:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:24:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:21] train_sft.py:596 - step 2607: {'train/loss': 0.02005159854888916, 'train/grad_norm': 0.46429476141929626, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:24:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:24:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:26] train_sft.py:596 - step 2608: {'train/loss': 0.014850437641143799, 'train/grad_norm': 0.38290923833847046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:24:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:24:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:31] train_sft.py:596 - step 2609: {'train/loss': 0.020375408232212067, 'train/grad_norm': 2.7813496589660645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:24:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:24:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:35] train_sft.py:596 - step 2610: {'train/loss': 0.02548779547214508, 'train/grad_norm': 0.7062897086143494, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:24:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:24:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:40] train_sft.py:596 - step 2611: {'train/loss': 0.0190717875957489, 'train/grad_norm': 0.35136812925338745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:24:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:24:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:44] train_sft.py:596 - step 2612: {'train/loss': 0.022703081369400024, 'train/grad_norm': 0.9296762943267822, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:24:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:24:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:49] train_sft.py:596 - step 2613: {'train/loss': 0.01855635643005371, 'train/grad_norm': 0.6698963642120361, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:24:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:24:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:54] train_sft.py:596 - step 2614: {'train/loss': 0.01918172836303711, 'train/grad_norm': 0.37648138403892517, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:24:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:24:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:24:59] train_sft.py:596 - step 2615: {'train/loss': 0.012520933523774147, 'train/grad_norm': 0.4610644280910492, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:24:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:24:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:03] train_sft.py:596 - step 2616: {'train/loss': 0.014277219772338867, 'train/grad_norm': 0.2906445860862732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:25:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:25:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:08] train_sft.py:596 - step 2617: {'train/loss': 0.04174518957734108, 'train/grad_norm': 0.453748494386673, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:25:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:25:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:13] train_sft.py:596 - step 2618: {'train/loss': 0.014441214501857758, 'train/grad_norm': 0.4891415536403656, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:25:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:25:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:18] train_sft.py:596 - step 2619: {'train/loss': 0.017890073359012604, 'train/grad_norm': 0.6191220283508301, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:25:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:25:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:23] train_sft.py:596 - step 2620: {'train/loss': 0.02068483829498291, 'train/grad_norm': 0.49349144101142883, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:25:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:25:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:28] train_sft.py:596 - step 2621: {'train/loss': 0.019472673535346985, 'train/grad_norm': 0.4154999554157257, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:25:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:25:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:33] train_sft.py:596 - step 2622: {'train/loss': 0.013458460569381714, 'train/grad_norm': 0.4037145972251892, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 15:25:33] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:25:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:37] train_sft.py:596 - step 2623: {'train/loss': 0.01943516731262207, 'train/grad_norm': 0.3947388827800751, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 15:25:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:25:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:42] train_sft.py:596 - step 2624: {'train/loss': 0.018512248992919922, 'train/grad_norm': 0.4776439368724823, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:25:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:25:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:47] train_sft.py:596 - step 2625: {'train/loss': 0.02074199914932251, 'train/grad_norm': 0.40022963285446167, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:25:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:25:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:51] train_sft.py:596 - step 2626: {'train/loss': 0.011894047260284424, 'train/grad_norm': 0.26597175002098083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:25:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:25:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:25:56] train_sft.py:596 - step 2627: {'train/loss': 0.016573593020439148, 'train/grad_norm': 0.723443329334259, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:25:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:25:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:01] train_sft.py:596 - step 2628: {'train/loss': 0.017292499542236328, 'train/grad_norm': 0.3690383732318878, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:26:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:26:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:05] train_sft.py:596 - step 2629: {'train/loss': 0.014201715588569641, 'train/grad_norm': 0.46823668479919434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:26:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:10] train_sft.py:596 - step 2630: {'train/loss': 0.02072371542453766, 'train/grad_norm': 0.48313286900520325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:26:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:15] train_sft.py:596 - step 2631: {'train/loss': 0.015239745378494263, 'train/grad_norm': 0.6260079145431519, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:26:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:26:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:19] train_sft.py:596 - step 2632: {'train/loss': 0.01682722568511963, 'train/grad_norm': 0.3981451988220215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:26:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:24] train_sft.py:596 - step 2633: {'train/loss': 0.014269143342971802, 'train/grad_norm': 0.3834926187992096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:26:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:26:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:29] train_sft.py:596 - step 2634: {'train/loss': 0.021813012659549713, 'train/grad_norm': 0.4729217290878296, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:26:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:34] train_sft.py:596 - step 2635: {'train/loss': 0.021809697151184082, 'train/grad_norm': 0.5023263096809387, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:26:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:26:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:38] train_sft.py:596 - step 2636: {'train/loss': 0.019981741905212402, 'train/grad_norm': 0.5420611500740051, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:26:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:43] train_sft.py:596 - step 2637: {'train/loss': 0.01735030859708786, 'train/grad_norm': 0.5431790351867676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:26:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:26:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:48] train_sft.py:596 - step 2638: {'train/loss': 0.021218597888946533, 'train/grad_norm': 0.4790101647377014, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:26:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:53] train_sft.py:596 - step 2639: {'train/loss': 0.01622571051120758, 'train/grad_norm': 0.31381502747535706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:26:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:26:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:26:57] train_sft.py:596 - step 2640: {'train/loss': 0.016455791890621185, 'train/grad_norm': 0.36642447113990784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:26:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:26:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:02] train_sft.py:596 - step 2641: {'train/loss': 0.017199933528900146, 'train/grad_norm': 0.44448137283325195, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:27:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:27:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:07] train_sft.py:596 - step 2642: {'train/loss': 0.01742255687713623, 'train/grad_norm': 0.41504454612731934, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 15:27:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:27:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:12] train_sft.py:596 - step 2643: {'train/loss': 0.01230427622795105, 'train/grad_norm': 0.4345075488090515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:27:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:27:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:17] train_sft.py:596 - step 2644: {'train/loss': 0.018910802900791168, 'train/grad_norm': 0.5716851353645325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:27:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:27:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:22] train_sft.py:596 - step 2645: {'train/loss': 0.021639704704284668, 'train/grad_norm': 0.5107713937759399, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:27:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:27:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:26] train_sft.py:596 - step 2646: {'train/loss': 0.022451400756835938, 'train/grad_norm': 0.3894806206226349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:27:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:27:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:31] train_sft.py:596 - step 2647: {'train/loss': 0.01383143663406372, 'train/grad_norm': 0.41214820742607117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:27:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:27:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:36] train_sft.py:596 - step 2648: {'train/loss': 0.022059082984924316, 'train/grad_norm': 0.6049631834030151, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:27:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:27:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:41] train_sft.py:596 - step 2649: {'train/loss': 0.021385759115219116, 'train/grad_norm': 0.8264197111129761, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:27:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:27:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:45] train_sft.py:596 - step 2650: {'train/loss': 0.016798272728919983, 'train/grad_norm': 0.3092796206474304, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:27:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:27:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:50] train_sft.py:596 - step 2651: {'train/loss': 0.030479729175567627, 'train/grad_norm': 0.9724225997924805, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:27:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:27:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:27:55] train_sft.py:596 - step 2652: {'train/loss': 0.019742459058761597, 'train/grad_norm': 0.5194236636161804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:27:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:27:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:00] train_sft.py:596 - step 2653: {'train/loss': 0.015726715326309204, 'train/grad_norm': 0.531383216381073, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 15:28:00] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:28:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:04] train_sft.py:596 - step 2654: {'train/loss': 0.017507284879684448, 'train/grad_norm': 0.3740888237953186, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:28:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:28:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:09] train_sft.py:596 - step 2655: {'train/loss': 0.02481028437614441, 'train/grad_norm': 0.6636464595794678, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 15:28:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:28:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:14] train_sft.py:596 - step 2656: {'train/loss': 0.0145978182554245, 'train/grad_norm': 0.43031370639801025, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:28:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:28:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:19] train_sft.py:596 - step 2657: {'train/loss': 0.018694937229156494, 'train/grad_norm': 0.595903754234314, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:28:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:28:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:24] train_sft.py:596 - step 2658: {'train/loss': 0.012543588876724243, 'train/grad_norm': 0.31740832328796387, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:28:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:28:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:29] train_sft.py:596 - step 2659: {'train/loss': 0.024780169129371643, 'train/grad_norm': 0.5022340416908264, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:28:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:28:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:33] train_sft.py:596 - step 2660: {'train/loss': 0.02444753795862198, 'train/grad_norm': 0.5715446472167969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:28:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:28:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:39] train_sft.py:596 - step 2661: {'train/loss': 0.022290527820587158, 'train/grad_norm': 0.5742290019989014, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 15:28:39] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:28:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:44] train_sft.py:596 - step 2662: {'train/loss': 0.023254692554473877, 'train/grad_norm': 0.5409842133522034, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 15:28:44] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:28:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:49] train_sft.py:596 - step 2663: {'train/loss': 0.023763716220855713, 'train/grad_norm': 0.5709773898124695, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 15:28:49] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:28:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:54] train_sft.py:596 - step 2664: {'train/loss': 0.021065056324005127, 'train/grad_norm': 0.558349072933197, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:28:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:28:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:28:59] train_sft.py:596 - step 2665: {'train/loss': 0.012871891260147095, 'train/grad_norm': 0.4092760682106018, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 15:28:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:28:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:03] train_sft.py:596 - step 2666: {'train/loss': 0.013780951499938965, 'train/grad_norm': 0.7306358814239502, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:29:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:29:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:08] train_sft.py:596 - step 2667: {'train/loss': 0.017546329647302628, 'train/grad_norm': 0.4514439105987549, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:29:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:29:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:13] train_sft.py:596 - step 2668: {'train/loss': 0.017630379647016525, 'train/grad_norm': 0.9547387361526489, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 15:29:13] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:29:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:18] train_sft.py:596 - step 2669: {'train/loss': 0.020685791969299316, 'train/grad_norm': 0.5990818738937378, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 15:29:18] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:29:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:23] train_sft.py:596 - step 2670: {'train/loss': 0.028761625289916992, 'train/grad_norm': 1.0154609680175781, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:29:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:29:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:28] train_sft.py:596 - step 2671: {'train/loss': 0.027894258499145508, 'train/grad_norm': 0.5743667483329773, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:29:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:29:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:33] train_sft.py:596 - step 2672: {'train/loss': 0.013186074793338776, 'train/grad_norm': 0.3271932601928711, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 15:29:33] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:29:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:38] train_sft.py:596 - step 2673: {'train/loss': 0.016968920826911926, 'train/grad_norm': 0.4481118321418762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:29:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:29:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:42] train_sft.py:596 - step 2674: {'train/loss': 0.027454495429992676, 'train/grad_norm': 0.5022950768470764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:29:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:29:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:47] train_sft.py:596 - step 2675: {'train/loss': 0.014880239963531494, 'train/grad_norm': 0.34665533900260925, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:29:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:29:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:52] train_sft.py:596 - step 2676: {'train/loss': 0.02875196933746338, 'train/grad_norm': 0.5043358206748962, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:29:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:29:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:29:57] train_sft.py:596 - step 2677: {'train/loss': 0.015019670128822327, 'train/grad_norm': 0.47110751271247864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 15:29:57] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:29:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:02] train_sft.py:596 - step 2678: {'train/loss': 0.019423326011747122, 'train/grad_norm': 0.4284949004650116, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 15:30:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:30:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:07] train_sft.py:596 - step 2679: {'train/loss': 0.025968775153160095, 'train/grad_norm': 0.7885586023330688, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:30:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:30:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:12] train_sft.py:596 - step 2680: {'train/loss': 0.013594329357147217, 'train/grad_norm': 0.33307963609695435, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 15:30:12] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:30:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:17] train_sft.py:596 - step 2681: {'train/loss': 0.014985427260398865, 'train/grad_norm': 0.4340936541557312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:30:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:30:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:22] train_sft.py:596 - step 2682: {'train/loss': 0.01755356788635254, 'train/grad_norm': 0.6390888094902039, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 15:30:22] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:30:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:27] train_sft.py:596 - step 2683: {'train/loss': 0.01533585786819458, 'train/grad_norm': 0.3426174521446228, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:30:27] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:30:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:32] train_sft.py:596 - step 2684: {'train/loss': 0.022186733782291412, 'train/grad_norm': 0.6398780345916748, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:30:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:30:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:37] train_sft.py:596 - step 2685: {'train/loss': 0.021578460931777954, 'train/grad_norm': 0.5376577377319336, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:30:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:30:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:41] train_sft.py:596 - step 2686: {'train/loss': 0.021642163395881653, 'train/grad_norm': 0.5320632457733154, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:30:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:30:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:46] train_sft.py:596 - step 2687: {'train/loss': 0.018487244844436646, 'train/grad_norm': 0.46124929189682007, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:30:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:30:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:51] train_sft.py:596 - step 2688: {'train/loss': 0.015382364392280579, 'train/grad_norm': 0.3171387016773224, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:30:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:30:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:30:56] train_sft.py:596 - step 2689: {'train/loss': 0.028395235538482666, 'train/grad_norm': 0.5763688087463379, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:30:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:30:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:01] train_sft.py:596 - step 2690: {'train/loss': 0.022794008255004883, 'train/grad_norm': 0.6462052464485168, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:31:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:31:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:05] train_sft.py:596 - step 2691: {'train/loss': 0.015880227088928223, 'train/grad_norm': 0.5493299961090088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 15:31:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:31:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:10] train_sft.py:596 - step 2692: {'train/loss': 0.01792719215154648, 'train/grad_norm': 0.4369785785675049, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 15:31:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:31:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:15] train_sft.py:596 - step 2693: {'train/loss': 0.016957178711891174, 'train/grad_norm': 0.5690022706985474, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:31:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:31:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:20] train_sft.py:596 - step 2694: {'train/loss': 0.01909118890762329, 'train/grad_norm': 0.459527850151062, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:31:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:31:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:25] train_sft.py:596 - step 2695: {'train/loss': 0.020036309957504272, 'train/grad_norm': 0.39050236344337463, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 15:31:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:31:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:30] train_sft.py:596 - step 2696: {'train/loss': 0.012772291898727417, 'train/grad_norm': 0.33343586325645447, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:31:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:31:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:34] train_sft.py:596 - step 2697: {'train/loss': 0.02044440060853958, 'train/grad_norm': 0.4153211712837219, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:31:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:31:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:39] train_sft.py:596 - step 2698: {'train/loss': 0.020493894815444946, 'train/grad_norm': 0.6155028939247131, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:31:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:31:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:44] train_sft.py:596 - step 2699: {'train/loss': 0.014031171798706055, 'train/grad_norm': 0.5317932367324829, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:31:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:31:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:49] train_sft.py:596 - step 2700: {'train/loss': 0.017654597759246826, 'train/grad_norm': 0.5026488900184631, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:31:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:31:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:53] train_sft.py:596 - step 2701: {'train/loss': 0.01757963001728058, 'train/grad_norm': 0.5335826873779297, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:31:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:31:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:31:58] train_sft.py:596 - step 2702: {'train/loss': 0.016448497772216797, 'train/grad_norm': 0.3043763339519501, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:31:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:31:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:03] train_sft.py:596 - step 2703: {'train/loss': 0.015111789107322693, 'train/grad_norm': 0.4267534017562866, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 15:32:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:32:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:07] train_sft.py:596 - step 2704: {'train/loss': 0.018585607409477234, 'train/grad_norm': 0.4942536950111389, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:32:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:32:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:12] train_sft.py:596 - step 2705: {'train/loss': 0.005901992321014404, 'train/grad_norm': 0.22480259835720062, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 15:32:12] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:32:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:17] train_sft.py:596 - step 2706: {'train/loss': 0.014444239437580109, 'train/grad_norm': 0.4365996718406677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:32:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:32:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:22] train_sft.py:596 - step 2707: {'train/loss': 0.018566787242889404, 'train/grad_norm': 0.48925232887268066, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:32:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:32:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:26] train_sft.py:596 - step 2708: {'train/loss': 0.01222778856754303, 'train/grad_norm': 0.5162798166275024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:32:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:32:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:32] train_sft.py:596 - step 2709: {'train/loss': 0.009188741445541382, 'train/grad_norm': 0.30277004837989807, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.25s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.25s/it]
[2025-12-29 15:32:32] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 15:32:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:36] train_sft.py:596 - step 2710: {'train/loss': 0.01208476722240448, 'train/grad_norm': 0.7898487448692322, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:32:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:32:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:41] train_sft.py:596 - step 2711: {'train/loss': 0.021285593509674072, 'train/grad_norm': 0.7888597249984741, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 15:32:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:32:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:46] train_sft.py:596 - step 2712: {'train/loss': 0.013583078980445862, 'train/grad_norm': 0.3849949538707733, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:32:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:32:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:51] train_sft.py:596 - step 2713: {'train/loss': 0.012983322143554688, 'train/grad_norm': 0.416993647813797, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:32:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:32:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:32:55] train_sft.py:596 - step 2714: {'train/loss': 0.018539294600486755, 'train/grad_norm': 0.4709826409816742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:32:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:32:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:00] train_sft.py:596 - step 2715: {'train/loss': 0.012707710266113281, 'train/grad_norm': 0.4704941213130951, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:33:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:33:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:05] train_sft.py:596 - step 2716: {'train/loss': 0.01088716834783554, 'train/grad_norm': 0.5282138586044312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:33:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:33:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:09] train_sft.py:596 - step 2717: {'train/loss': 0.012838900089263916, 'train/grad_norm': 0.45310476422309875, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:33:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:33:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:14] train_sft.py:596 - step 2718: {'train/loss': 0.008990228176116943, 'train/grad_norm': 0.24484775960445404, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:33:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:33:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:19] train_sft.py:596 - step 2719: {'train/loss': 0.007514744997024536, 'train/grad_norm': 0.17630305886268616, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 15:33:19] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:33:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:24] train_sft.py:596 - step 2720: {'train/loss': 0.010377585887908936, 'train/grad_norm': 0.28710728883743286, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:33:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:33:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:28] train_sft.py:596 - step 2721: {'train/loss': 0.044247725047171116, 'train/grad_norm': 0.9426742792129517, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 15:33:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:33:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:34] train_sft.py:596 - step 2722: {'train/loss': 0.012881554663181305, 'train/grad_norm': 0.3642219007015228, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.01s/it]
[2025-12-29 15:33:34] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:33:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:38] train_sft.py:596 - step 2723: {'train/loss': 0.013470694422721863, 'train/grad_norm': 0.2832401692867279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:33:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:33:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:43] train_sft.py:596 - step 2724: {'train/loss': 0.019785143435001373, 'train/grad_norm': 0.888350784778595, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 15:33:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:33:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:48] train_sft.py:596 - step 2725: {'train/loss': 0.012110143899917603, 'train/grad_norm': 0.7876073718070984, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:33:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:33:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:53] train_sft.py:596 - step 2726: {'train/loss': 0.01625378429889679, 'train/grad_norm': 0.3914799690246582, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:33:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:33:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:33:57] train_sft.py:596 - step 2727: {'train/loss': 0.017697475850582123, 'train/grad_norm': 0.3995678126811981, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:33:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:33:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:02] train_sft.py:596 - step 2728: {'train/loss': 0.01620890200138092, 'train/grad_norm': 0.40009820461273193, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:34:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:34:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:07] train_sft.py:596 - step 2729: {'train/loss': 0.010486289858818054, 'train/grad_norm': 0.2814573645591736, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:34:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:34:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:12] train_sft.py:596 - step 2730: {'train/loss': 0.01313181221485138, 'train/grad_norm': 0.5398860573768616, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:34:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:34:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:16] train_sft.py:596 - step 2731: {'train/loss': 0.016251668334007263, 'train/grad_norm': 0.4839341342449188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:34:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:34:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:21] train_sft.py:596 - step 2732: {'train/loss': 0.016025274991989136, 'train/grad_norm': 0.648845374584198, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:34:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:34:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:25] train_sft.py:596 - step 2733: {'train/loss': 0.015798907727003098, 'train/grad_norm': 0.39348238706588745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:34:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:34:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:30] train_sft.py:596 - step 2734: {'train/loss': 0.0180651992559433, 'train/grad_norm': 0.5134351253509521, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 15:34:30] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:34:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:35] train_sft.py:596 - step 2735: {'train/loss': 0.010019220411777496, 'train/grad_norm': 0.3246139585971832, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:34:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:34:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:40] train_sft.py:596 - step 2736: {'train/loss': 0.010975152254104614, 'train/grad_norm': 0.29955488443374634, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:34:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:34:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:45] train_sft.py:596 - step 2737: {'train/loss': 0.009968392550945282, 'train/grad_norm': 0.2797282338142395, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 15:34:45] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:34:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:49] train_sft.py:596 - step 2738: {'train/loss': 0.02228716015815735, 'train/grad_norm': 0.5365000367164612, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:34:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:34:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:54] train_sft.py:596 - step 2739: {'train/loss': 0.021466583013534546, 'train/grad_norm': 0.5010703802108765, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:34:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:34:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:34:59] train_sft.py:596 - step 2740: {'train/loss': 0.008547801524400711, 'train/grad_norm': 0.6011211276054382, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:34:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:34:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:03] train_sft.py:596 - step 2741: {'train/loss': 0.014605313539505005, 'train/grad_norm': 0.46627575159072876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:35:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:35:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:08] train_sft.py:596 - step 2742: {'train/loss': 0.01653936505317688, 'train/grad_norm': 0.6225442886352539, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:35:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:35:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:13] train_sft.py:596 - step 2743: {'train/loss': 0.011356133967638016, 'train/grad_norm': 0.25833413004875183, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:35:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:35:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:17] train_sft.py:596 - step 2744: {'train/loss': 0.020367145538330078, 'train/grad_norm': 0.5668578147888184, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:35:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:35:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:22] train_sft.py:596 - step 2745: {'train/loss': 0.012214813381433487, 'train/grad_norm': 0.2848428189754486, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:35:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:35:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:27] train_sft.py:596 - step 2746: {'train/loss': 0.014255300164222717, 'train/grad_norm': 0.382388174533844, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:35:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:35:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:31] train_sft.py:596 - step 2747: {'train/loss': 0.011930704116821289, 'train/grad_norm': 0.3346927762031555, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:35:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:35:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:36] train_sft.py:596 - step 2748: {'train/loss': 0.00878097116947174, 'train/grad_norm': 0.19645294547080994, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:35:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:35:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:41] train_sft.py:596 - step 2749: {'train/loss': 0.013460151851177216, 'train/grad_norm': 0.4736301302909851, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 15:35:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:35:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:46] train_sft.py:596 - step 2750: {'train/loss': 0.014185324311256409, 'train/grad_norm': 0.390724241733551, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:35:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:35:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:50] train_sft.py:596 - step 2751: {'train/loss': 0.014789188280701637, 'train/grad_norm': 0.3208478093147278, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 15:35:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:35:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:35:55] train_sft.py:596 - step 2752: {'train/loss': 0.019941478967666626, 'train/grad_norm': 0.4910266399383545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:35:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:35:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:00] train_sft.py:596 - step 2753: {'train/loss': 0.017531156539916992, 'train/grad_norm': 0.5970969200134277, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:36:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:36:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:04] train_sft.py:596 - step 2754: {'train/loss': 0.020594894886016846, 'train/grad_norm': 0.5272741913795471, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:36:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:36:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:09] train_sft.py:596 - step 2755: {'train/loss': 0.01419954001903534, 'train/grad_norm': 0.40920963883399963, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 15:36:09] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 15:36:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:14] train_sft.py:596 - step 2756: {'train/loss': 0.014317035675048828, 'train/grad_norm': 0.3919953405857086, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:36:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:36:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:18] train_sft.py:596 - step 2757: {'train/loss': 0.010384663939476013, 'train/grad_norm': 0.40020692348480225, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:36:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:36:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:23] train_sft.py:596 - step 2758: {'train/loss': 0.010488495230674744, 'train/grad_norm': 0.4055052399635315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:36:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:36:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:28] train_sft.py:596 - step 2759: {'train/loss': 0.009563155472278595, 'train/grad_norm': 0.3169328272342682, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:36:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:36:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:32] train_sft.py:596 - step 2760: {'train/loss': 0.02120530605316162, 'train/grad_norm': 0.6759072542190552, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:36:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:36:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:37] train_sft.py:596 - step 2761: {'train/loss': 0.012810081243515015, 'train/grad_norm': 0.2232208400964737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:36:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:36:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:42] train_sft.py:596 - step 2762: {'train/loss': 0.01208871603012085, 'train/grad_norm': 0.3318054676055908, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:36:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:36:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:46] train_sft.py:596 - step 2763: {'train/loss': 0.011742940172553062, 'train/grad_norm': 0.39233657717704773, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:36:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:36:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:51] train_sft.py:596 - step 2764: {'train/loss': 0.009811758995056152, 'train/grad_norm': 0.33679571747779846, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 15:36:51] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:36:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:36:56] train_sft.py:596 - step 2765: {'train/loss': 0.01063622534275055, 'train/grad_norm': 0.4445091187953949, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:36:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:36:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:01] train_sft.py:596 - step 2766: {'train/loss': 0.0168493390083313, 'train/grad_norm': 0.5626474022865295, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:37:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:37:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:05] train_sft.py:596 - step 2767: {'train/loss': 0.015441209077835083, 'train/grad_norm': 0.5151094198226929, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:37:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:37:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:10] train_sft.py:596 - step 2768: {'train/loss': 0.013928830623626709, 'train/grad_norm': 0.5379305481910706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:37:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:15] train_sft.py:596 - step 2769: {'train/loss': 0.022752180695533752, 'train/grad_norm': 0.6004962921142578, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:37:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:37:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:20] train_sft.py:596 - step 2770: {'train/loss': 0.014835461974143982, 'train/grad_norm': 0.40752512216567993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:37:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:37:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:24] train_sft.py:596 - step 2771: {'train/loss': 0.011318288743495941, 'train/grad_norm': 0.46078917384147644, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:37:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:29] train_sft.py:596 - step 2772: {'train/loss': 0.01534530520439148, 'train/grad_norm': 0.405403733253479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:37:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:37:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:34] train_sft.py:596 - step 2773: {'train/loss': 0.012284886091947556, 'train/grad_norm': 0.5310534238815308, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:37:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:39] train_sft.py:596 - step 2774: {'train/loss': 0.02491319179534912, 'train/grad_norm': 0.5137521028518677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:37:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:43] train_sft.py:596 - step 2775: {'train/loss': 0.019097961485385895, 'train/grad_norm': 0.48115411400794983, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:37:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:37:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:48] train_sft.py:596 - step 2776: {'train/loss': 0.012618273496627808, 'train/grad_norm': 0.4550393521785736, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:37:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:52] train_sft.py:596 - step 2777: {'train/loss': 0.01950204372406006, 'train/grad_norm': 0.4961414337158203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:37:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:37:57] train_sft.py:596 - step 2778: {'train/loss': 0.012511968612670898, 'train/grad_norm': 0.32427477836608887, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:37:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:37:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:02] train_sft.py:596 - step 2779: {'train/loss': 0.016670212149620056, 'train/grad_norm': 0.6867178082466125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:38:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:38:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:06] train_sft.py:596 - step 2780: {'train/loss': 0.01568751037120819, 'train/grad_norm': 0.3466831147670746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:38:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:38:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:11] train_sft.py:596 - step 2781: {'train/loss': 0.011216431856155396, 'train/grad_norm': 0.3685505986213684, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 15:38:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:38:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:16] train_sft.py:596 - step 2782: {'train/loss': 0.03061121702194214, 'train/grad_norm': 1.0688717365264893, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:38:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:38:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:21] train_sft.py:596 - step 2783: {'train/loss': 0.011585474014282227, 'train/grad_norm': 0.3268266022205353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:38:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:38:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:25] train_sft.py:596 - step 2784: {'train/loss': 0.015132546424865723, 'train/grad_norm': 0.47041213512420654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:38:25] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:38:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:30] train_sft.py:596 - step 2785: {'train/loss': 0.016684390604496002, 'train/grad_norm': 0.6040070056915283, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:38:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:38:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:35] train_sft.py:596 - step 2786: {'train/loss': 0.016550257802009583, 'train/grad_norm': 0.3788755238056183, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:38:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:38:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:40] train_sft.py:596 - step 2787: {'train/loss': 0.020763278007507324, 'train/grad_norm': 0.44976070523262024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 15:38:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:38:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:44] train_sft.py:596 - step 2788: {'train/loss': 0.017751917243003845, 'train/grad_norm': 0.4014503061771393, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:38:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:38:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:49] train_sft.py:596 - step 2789: {'train/loss': 0.0159255713224411, 'train/grad_norm': 0.33671045303344727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:38:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:38:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:54] train_sft.py:596 - step 2790: {'train/loss': 0.012442708015441895, 'train/grad_norm': 0.38204213976860046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:38:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:38:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:38:59] train_sft.py:596 - step 2791: {'train/loss': 0.01698058843612671, 'train/grad_norm': 0.45643410086631775, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:38:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:38:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:04] train_sft.py:596 - step 2792: {'train/loss': 0.025307834148406982, 'train/grad_norm': 0.5293824076652527, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 15:39:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:39:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:08] train_sft.py:596 - step 2793: {'train/loss': 0.02236795425415039, 'train/grad_norm': 0.5197558403015137, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:39:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:39:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:13] train_sft.py:596 - step 2794: {'train/loss': 0.015574786812067032, 'train/grad_norm': 0.47242483496665955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:39:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:39:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:17] train_sft.py:596 - step 2795: {'train/loss': 0.013876814395189285, 'train/grad_norm': 0.7110926508903503, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:39:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:39:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:22] train_sft.py:596 - step 2796: {'train/loss': 0.010541252791881561, 'train/grad_norm': 0.29092147946357727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:39:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:39:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:27] train_sft.py:596 - step 2797: {'train/loss': 0.016497671604156494, 'train/grad_norm': 0.39097869396209717, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:39:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:39:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:32] train_sft.py:596 - step 2798: {'train/loss': 0.017534688115119934, 'train/grad_norm': 0.6035396456718445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 15:39:32] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:39:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:36] train_sft.py:596 - step 2799: {'train/loss': 0.03021600842475891, 'train/grad_norm': 0.7042227387428284, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:39:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:39:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:41] train_sft.py:596 - step 2800: {'train/loss': 0.01809707283973694, 'train/grad_norm': 0.5390011072158813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:39:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:39:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:46] train_sft.py:596 - step 2801: {'train/loss': 0.014509201049804688, 'train/grad_norm': 0.5736780762672424, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:39:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:39:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:50] train_sft.py:596 - step 2802: {'train/loss': 0.015616148710250854, 'train/grad_norm': 0.42598408460617065, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:39:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:39:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:39:55] train_sft.py:596 - step 2803: {'train/loss': 0.015727713704109192, 'train/grad_norm': 0.5529630184173584, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 15:39:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:39:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:00] train_sft.py:596 - step 2804: {'train/loss': 0.02130049467086792, 'train/grad_norm': 0.3955613374710083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:40:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:40:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:04] train_sft.py:596 - step 2805: {'train/loss': 0.017296135425567627, 'train/grad_norm': 0.43343159556388855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:40:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:40:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:09] train_sft.py:596 - step 2806: {'train/loss': 0.010745182633399963, 'train/grad_norm': 0.21491345763206482, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 15:40:09] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:40:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:14] train_sft.py:596 - step 2807: {'train/loss': 0.017511919140815735, 'train/grad_norm': 0.6840752959251404, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:40:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:40:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:19] train_sft.py:596 - step 2808: {'train/loss': 0.01718858629465103, 'train/grad_norm': 0.5019926428794861, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:40:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:40:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:23] train_sft.py:596 - step 2809: {'train/loss': 0.021360641345381737, 'train/grad_norm': 0.49944740533828735, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:40:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:40:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:28] train_sft.py:596 - step 2810: {'train/loss': 0.026920601725578308, 'train/grad_norm': 0.8378915786743164, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:40:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:40:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:33] train_sft.py:596 - step 2811: {'train/loss': 0.011262714862823486, 'train/grad_norm': 0.2994017004966736, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:40:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:40:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:38] train_sft.py:596 - step 2812: {'train/loss': 0.01307058334350586, 'train/grad_norm': 0.303723007440567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 15:40:38] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:40:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:43] train_sft.py:596 - step 2813: {'train/loss': 0.014584392309188843, 'train/grad_norm': 0.5546528100967407, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:40:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:40:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:48] train_sft.py:596 - step 2814: {'train/loss': 0.02472376823425293, 'train/grad_norm': 0.5869102478027344, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 15:40:48] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:40:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:53] train_sft.py:596 - step 2815: {'train/loss': 0.015916138887405396, 'train/grad_norm': 0.4078325927257538, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 15:40:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:40:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:40:58] train_sft.py:596 - step 2816: {'train/loss': 0.017929792404174805, 'train/grad_norm': 0.5839073657989502, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.19s/it]
[2025-12-29 15:40:58] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 15:40:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:03] train_sft.py:596 - step 2817: {'train/loss': 0.02275097370147705, 'train/grad_norm': 0.6268178224563599, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:41:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:08] train_sft.py:596 - step 2818: {'train/loss': 0.01678141951560974, 'train/grad_norm': 0.6125489473342896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:41:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:41:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:12] train_sft.py:596 - step 2819: {'train/loss': 0.012762770056724548, 'train/grad_norm': 0.42086055874824524, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:41:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:17] train_sft.py:596 - step 2820: {'train/loss': 0.020632855594158173, 'train/grad_norm': 0.8590975403785706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 15:41:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:41:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:22] train_sft.py:596 - step 2821: {'train/loss': 0.011232145130634308, 'train/grad_norm': 0.2560289204120636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:41:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:26] train_sft.py:596 - step 2822: {'train/loss': 0.014844253659248352, 'train/grad_norm': 0.38337239623069763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:41:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:31] train_sft.py:596 - step 2823: {'train/loss': 0.021589413285255432, 'train/grad_norm': 0.5170880556106567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:41:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:36] train_sft.py:596 - step 2824: {'train/loss': 0.01233518123626709, 'train/grad_norm': 0.23991446197032928, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:41:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:41:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:41] train_sft.py:596 - step 2825: {'train/loss': 0.01343264989554882, 'train/grad_norm': 0.5453678369522095, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 15:41:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:41:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:46] train_sft.py:596 - step 2826: {'train/loss': 0.017104372382164, 'train/grad_norm': 0.3816452920436859, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 15:41:46] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:41:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:50] train_sft.py:596 - step 2827: {'train/loss': 0.02938617765903473, 'train/grad_norm': 0.6218134760856628, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:41:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:41:55] train_sft.py:596 - step 2828: {'train/loss': 0.013358473777770996, 'train/grad_norm': 0.3254944384098053, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:41:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:41:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:00] train_sft.py:596 - step 2829: {'train/loss': 0.010029241442680359, 'train/grad_norm': 0.3611936569213867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:42:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:42:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:04] train_sft.py:596 - step 2830: {'train/loss': 0.013640239834785461, 'train/grad_norm': 0.41847559809684753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:42:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:42:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:09] train_sft.py:596 - step 2831: {'train/loss': 0.012302804738283157, 'train/grad_norm': 0.35201042890548706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:42:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:42:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:14] train_sft.py:596 - step 2832: {'train/loss': 0.02074369043111801, 'train/grad_norm': 0.556336522102356, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:42:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:42:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:19] train_sft.py:596 - step 2833: {'train/loss': 0.014622539281845093, 'train/grad_norm': 0.3065996468067169, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:42:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:42:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:24] train_sft.py:596 - step 2834: {'train/loss': 0.013694189488887787, 'train/grad_norm': 0.3087511360645294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:42:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:42:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:28] train_sft.py:596 - step 2835: {'train/loss': 0.01573844999074936, 'train/grad_norm': 0.369394451379776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:42:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:42:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:33] train_sft.py:596 - step 2836: {'train/loss': 0.01865425705909729, 'train/grad_norm': 0.3015696108341217, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 15:42:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:42:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:38] train_sft.py:596 - step 2837: {'train/loss': 0.01373407244682312, 'train/grad_norm': 0.5487460494041443, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:42:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:42:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:43] train_sft.py:596 - step 2838: {'train/loss': 0.016838274896144867, 'train/grad_norm': 0.44971635937690735, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:42:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:42:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:48] train_sft.py:596 - step 2839: {'train/loss': 0.020705267786979675, 'train/grad_norm': 0.6979851126670837, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 15:42:48] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:42:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:52] train_sft.py:596 - step 2840: {'train/loss': 0.016831710934638977, 'train/grad_norm': 0.5308552384376526, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:42:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:42:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:42:57] train_sft.py:596 - step 2841: {'train/loss': 0.015476193279027939, 'train/grad_norm': 0.39338254928588867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 15:42:57] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:42:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:02] train_sft.py:596 - step 2842: {'train/loss': 0.01415560394525528, 'train/grad_norm': 0.3845579922199249, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 15:43:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:43:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:07] train_sft.py:596 - step 2843: {'train/loss': 0.01692521572113037, 'train/grad_norm': 0.46485385298728943, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:43:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:43:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:12] train_sft.py:596 - step 2844: {'train/loss': 0.01859182119369507, 'train/grad_norm': 0.6317996978759766, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 15:43:12] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:43:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:16] train_sft.py:596 - step 2845: {'train/loss': 0.01761995255947113, 'train/grad_norm': 0.4644100069999695, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:43:16] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:43:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:21] train_sft.py:596 - step 2846: {'train/loss': 0.01196432113647461, 'train/grad_norm': 0.31967321038246155, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 15:43:21] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:43:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:26] train_sft.py:596 - step 2847: {'train/loss': 0.01636393368244171, 'train/grad_norm': 0.40697982907295227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:43:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:43:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:31] train_sft.py:596 - step 2848: {'train/loss': 0.015404023230075836, 'train/grad_norm': 0.46927618980407715, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:43:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:43:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:36] train_sft.py:596 - step 2849: {'train/loss': 0.01003602147102356, 'train/grad_norm': 0.2854641079902649, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 15:43:36] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:43:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:41] train_sft.py:596 - step 2850: {'train/loss': 0.012815356254577637, 'train/grad_norm': 0.4330486059188843, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 15:43:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:43:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:45] train_sft.py:596 - step 2851: {'train/loss': 0.01603144407272339, 'train/grad_norm': 0.4394223093986511, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:43:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:43:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:50] train_sft.py:596 - step 2852: {'train/loss': 0.018992379307746887, 'train/grad_norm': 0.45125794410705566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:43:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:43:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:43:56] train_sft.py:596 - step 2853: {'train/loss': 0.02254459261894226, 'train/grad_norm': 0.4892613887786865, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 15:43:56] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:43:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:00] train_sft.py:596 - step 2854: {'train/loss': 0.013407405465841293, 'train/grad_norm': 0.3839566111564636, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:44:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:44:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:05] train_sft.py:596 - step 2855: {'train/loss': 0.012562310323119164, 'train/grad_norm': 0.39229002594947815, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 15:44:05] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:44:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:10] train_sft.py:596 - step 2856: {'train/loss': 0.017276342026889324, 'train/grad_norm': 0.43399202823638916, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.00s/it]
[2025-12-29 15:44:10] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:44:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:15] train_sft.py:596 - step 2857: {'train/loss': 0.019454628229141235, 'train/grad_norm': 0.4888896048069, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:44:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:44:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:20] train_sft.py:596 - step 2858: {'train/loss': 0.017282843589782715, 'train/grad_norm': 0.39701008796691895, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 15:44:20] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:44:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:25] train_sft.py:596 - step 2859: {'train/loss': 0.016476303339004517, 'train/grad_norm': 0.3308993875980377, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:44:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:44:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:30] train_sft.py:596 - step 2860: {'train/loss': 0.012765318155288696, 'train/grad_norm': 0.3999235928058624, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:44:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:44:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:35] train_sft.py:596 - step 2861: {'train/loss': 0.021946609020233154, 'train/grad_norm': 0.5709412693977356, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:44:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:44:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:40] train_sft.py:596 - step 2862: {'train/loss': 0.023354321718215942, 'train/grad_norm': 0.7861791849136353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:44:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:44:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:44] train_sft.py:596 - step 2863: {'train/loss': 0.01891002058982849, 'train/grad_norm': 0.4278604984283447, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:44:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:44:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:49] train_sft.py:596 - step 2864: {'train/loss': 0.010855838656425476, 'train/grad_norm': 0.2295626550912857, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:44:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:44:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:54] train_sft.py:596 - step 2865: {'train/loss': 0.01211819052696228, 'train/grad_norm': 0.2796306908130646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:44:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:44:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:44:59] train_sft.py:596 - step 2866: {'train/loss': 0.01309267058968544, 'train/grad_norm': 0.42807963490486145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.04s/it]
[2025-12-29 15:44:59] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:44:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:04] train_sft.py:596 - step 2867: {'train/loss': 0.01627728808671236, 'train/grad_norm': 0.5138584971427917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 15:45:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:45:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:09] train_sft.py:596 - step 2868: {'train/loss': 0.020598366856575012, 'train/grad_norm': 0.48908501863479614, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:45:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:45:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:14] train_sft.py:596 - step 2869: {'train/loss': 0.022477492690086365, 'train/grad_norm': 0.41849929094314575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:45:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:45:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:18] train_sft.py:596 - step 2870: {'train/loss': 0.01076577790081501, 'train/grad_norm': 0.34921008348464966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:45:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:45:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:23] train_sft.py:596 - step 2871: {'train/loss': 0.02313762903213501, 'train/grad_norm': 0.6871405839920044, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:45:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:45:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:28] train_sft.py:596 - step 2872: {'train/loss': 0.02284492552280426, 'train/grad_norm': 0.5626032948493958, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:45:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:45:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:33] train_sft.py:596 - step 2873: {'train/loss': 0.02489185333251953, 'train/grad_norm': 0.47586074471473694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:45:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:45:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:38] train_sft.py:596 - step 2874: {'train/loss': 0.011748507618904114, 'train/grad_norm': 0.6481825709342957, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 15:45:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:45:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:43] train_sft.py:596 - step 2875: {'train/loss': 0.024027913808822632, 'train/grad_norm': 0.7074705958366394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 15:45:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:45:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:48] train_sft.py:596 - step 2876: {'train/loss': 0.015027254819869995, 'train/grad_norm': 0.5416165590286255, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 15:45:48] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:45:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:52] train_sft.py:596 - step 2877: {'train/loss': 0.011741816997528076, 'train/grad_norm': 0.2655101716518402, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:45:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:45:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:45:57] train_sft.py:596 - step 2878: {'train/loss': 0.025820612907409668, 'train/grad_norm': 0.5755924582481384, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 15:45:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:45:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:02] train_sft.py:596 - step 2879: {'train/loss': 0.016812065616250038, 'train/grad_norm': 0.48679566383361816, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:46:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:46:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:07] train_sft.py:596 - step 2880: {'train/loss': 0.02086251974105835, 'train/grad_norm': 0.4633568227291107, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:46:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:46:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:12] train_sft.py:596 - step 2881: {'train/loss': 0.014977414160966873, 'train/grad_norm': 0.5443488955497742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 15:46:12] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:46:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:17] train_sft.py:596 - step 2882: {'train/loss': 0.01435907743871212, 'train/grad_norm': 0.350965678691864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:46:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:46:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:22] train_sft.py:596 - step 2883: {'train/loss': 0.018485337495803833, 'train/grad_norm': 0.4332704246044159, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:46:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:46:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:26] train_sft.py:596 - step 2884: {'train/loss': 0.013681456446647644, 'train/grad_norm': 0.5566644072532654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:46:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:46:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:31] train_sft.py:596 - step 2885: {'train/loss': 0.01954120397567749, 'train/grad_norm': 0.5105004906654358, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:46:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:46:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:36] train_sft.py:596 - step 2886: {'train/loss': 0.013068228960037231, 'train/grad_norm': 0.40215635299682617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:46:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:46:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:40] train_sft.py:596 - step 2887: {'train/loss': 0.017507001757621765, 'train/grad_norm': 0.4523308575153351, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:46:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:46:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:45] train_sft.py:596 - step 2888: {'train/loss': 0.022875994443893433, 'train/grad_norm': 0.5204212665557861, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:46:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:46:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:50] train_sft.py:596 - step 2889: {'train/loss': 0.010321345180273056, 'train/grad_norm': 0.2802843749523163, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:46:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:46:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:46:55] train_sft.py:596 - step 2890: {'train/loss': 0.014313369989395142, 'train/grad_norm': 0.43456795811653137, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:46:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:46:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:00] train_sft.py:596 - step 2891: {'train/loss': 0.016456328332424164, 'train/grad_norm': 0.39627495408058167, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:47:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:47:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:04] train_sft.py:596 - step 2892: {'train/loss': 0.0194472074508667, 'train/grad_norm': 0.5586496591567993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:47:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:47:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:09] train_sft.py:596 - step 2893: {'train/loss': 0.014192402362823486, 'train/grad_norm': 0.39632466435432434, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 15:47:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:47:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:14] train_sft.py:596 - step 2894: {'train/loss': 0.01375916600227356, 'train/grad_norm': 0.3186906576156616, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:47:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:47:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:19] train_sft.py:596 - step 2895: {'train/loss': 0.017610132694244385, 'train/grad_norm': 0.48984524607658386, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:47:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:47:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:23] train_sft.py:596 - step 2896: {'train/loss': 0.01657681167125702, 'train/grad_norm': 0.5658383965492249, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:47:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:47:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:28] train_sft.py:596 - step 2897: {'train/loss': 0.014541395008563995, 'train/grad_norm': 0.27726954221725464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 15:47:28] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:47:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:33] train_sft.py:596 - step 2898: {'train/loss': 0.02584768831729889, 'train/grad_norm': 0.5425009727478027, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:47:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:47:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:38] train_sft.py:596 - step 2899: {'train/loss': 0.0177488774061203, 'train/grad_norm': 0.44648611545562744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:47:38] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:47:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:43] train_sft.py:596 - step 2900: {'train/loss': 0.02590826153755188, 'train/grad_norm': 0.6529975533485413, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 15:47:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:47:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:47] train_sft.py:596 - step 2901: {'train/loss': 0.017315663397312164, 'train/grad_norm': 0.46793484687805176, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:47:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:47:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:52] train_sft.py:596 - step 2902: {'train/loss': 0.014007702469825745, 'train/grad_norm': 0.33301904797554016, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:47:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:47:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:47:57] train_sft.py:596 - step 2903: {'train/loss': 0.01664803922176361, 'train/grad_norm': 0.33605295419692993, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 15:47:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:47:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:01] train_sft.py:596 - step 2904: {'train/loss': 0.01618046034127474, 'train/grad_norm': 0.3379466235637665, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:48:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:48:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:06] train_sft.py:596 - step 2905: {'train/loss': 0.017545759677886963, 'train/grad_norm': 0.5407343506813049, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 15:48:06] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:48:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:11] train_sft.py:596 - step 2906: {'train/loss': 0.014047257602214813, 'train/grad_norm': 0.6551948189735413, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:48:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:48:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:16] train_sft.py:596 - step 2907: {'train/loss': 0.01710360497236252, 'train/grad_norm': 0.6237201690673828, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 15:48:16] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 15:48:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:21] train_sft.py:596 - step 2908: {'train/loss': 0.01830717921257019, 'train/grad_norm': 0.5469502806663513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:48:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:48:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:25] train_sft.py:596 - step 2909: {'train/loss': 0.024035334587097168, 'train/grad_norm': 0.6150976419448853, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:48:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:48:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:30] train_sft.py:596 - step 2910: {'train/loss': 0.020270824432373047, 'train/grad_norm': 0.43182915449142456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:48:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:48:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:35] train_sft.py:596 - step 2911: {'train/loss': 0.015317097306251526, 'train/grad_norm': 0.4747868776321411, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:48:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:48:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:39] train_sft.py:596 - step 2912: {'train/loss': 0.017456278204917908, 'train/grad_norm': 0.36684685945510864, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 15:48:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:48:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:44] train_sft.py:596 - step 2913: {'train/loss': 0.01461520791053772, 'train/grad_norm': 0.3457415997982025, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:48:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:48:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:49] train_sft.py:596 - step 2914: {'train/loss': 0.018064826726913452, 'train/grad_norm': 0.7092196345329285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:48:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:48:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:54] train_sft.py:596 - step 2915: {'train/loss': 0.017538174986839294, 'train/grad_norm': 0.6273728609085083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:48:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:48:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:48:58] train_sft.py:596 - step 2916: {'train/loss': 0.016884177923202515, 'train/grad_norm': 0.5639007091522217, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:48:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:48:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:03] train_sft.py:596 - step 2917: {'train/loss': 0.018591701984405518, 'train/grad_norm': 0.43060365319252014, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:49:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:49:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:08] train_sft.py:596 - step 2918: {'train/loss': 0.01232922077178955, 'train/grad_norm': 0.42410433292388916, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:49:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:49:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:12] train_sft.py:596 - step 2919: {'train/loss': 0.019378870725631714, 'train/grad_norm': 0.5835922360420227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:49:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:49:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:17] train_sft.py:596 - step 2920: {'train/loss': 0.01320183277130127, 'train/grad_norm': 0.5008074641227722, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:49:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:49:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:22] train_sft.py:596 - step 2921: {'train/loss': 0.01669703796505928, 'train/grad_norm': 0.6809306144714355, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 15:49:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:49:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:27] train_sft.py:596 - step 2922: {'train/loss': 0.019243826158344746, 'train/grad_norm': 0.412324994802475, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 15:49:27] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:49:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:32] train_sft.py:596 - step 2923: {'train/loss': 0.01747104525566101, 'train/grad_norm': 0.5939579606056213, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 15:49:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:49:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:36] train_sft.py:596 - step 2924: {'train/loss': 0.015140622854232788, 'train/grad_norm': 0.3679518401622772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:49:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:49:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:41] train_sft.py:596 - step 2925: {'train/loss': 0.019115231931209564, 'train/grad_norm': 0.4738844037055969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 15:49:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:49:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:46] train_sft.py:596 - step 2926: {'train/loss': 0.024634480476379395, 'train/grad_norm': 0.8107976317405701, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 15:49:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:49:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:51] train_sft.py:596 - step 2927: {'train/loss': 0.01849372824653983, 'train/grad_norm': 0.4299692213535309, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:49:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:49:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:49:55] train_sft.py:596 - step 2928: {'train/loss': 0.013156518340110779, 'train/grad_norm': 0.20967267453670502, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:49:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:49:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:00] train_sft.py:596 - step 2929: {'train/loss': 0.008476532995700836, 'train/grad_norm': 0.3489513099193573, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:50:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:50:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:05] train_sft.py:596 - step 2930: {'train/loss': 0.012637544423341751, 'train/grad_norm': 0.31675058603286743, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:50:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:50:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:09] train_sft.py:596 - step 2931: {'train/loss': 0.010035395622253418, 'train/grad_norm': 0.42180705070495605, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:50:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:50:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:14] train_sft.py:596 - step 2932: {'train/loss': 0.00798855721950531, 'train/grad_norm': 0.23185785114765167, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:50:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:50:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:19] train_sft.py:596 - step 2933: {'train/loss': 0.012320876121520996, 'train/grad_norm': 0.2910774350166321, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:50:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:50:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:23] train_sft.py:596 - step 2934: {'train/loss': 0.010778650641441345, 'train/grad_norm': 0.4455278813838959, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:50:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:50:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:28] train_sft.py:596 - step 2935: {'train/loss': 0.02079033851623535, 'train/grad_norm': 0.76658695936203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:50:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:50:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:33] train_sft.py:596 - step 2936: {'train/loss': 0.012548081576824188, 'train/grad_norm': 0.3833296597003937, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 15:50:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:50:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:37] train_sft.py:596 - step 2937: {'train/loss': 0.013978570699691772, 'train/grad_norm': 0.6774721741676331, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:50:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:50:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:42] train_sft.py:596 - step 2938: {'train/loss': 0.02147217094898224, 'train/grad_norm': 0.781374454498291, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 15:50:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:50:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:47] train_sft.py:596 - step 2939: {'train/loss': 0.009068713523447514, 'train/grad_norm': 0.19729191064834595, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 15:50:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:50:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:51] train_sft.py:596 - step 2940: {'train/loss': 0.0066118016839027405, 'train/grad_norm': 0.38032266497612, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 15:50:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:50:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:50:56] train_sft.py:596 - step 2941: {'train/loss': 0.024679988622665405, 'train/grad_norm': 0.4829779267311096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:50:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:50:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:01] train_sft.py:596 - step 2942: {'train/loss': 0.01570357382297516, 'train/grad_norm': 0.4447474181652069, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:51:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:51:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:05] train_sft.py:596 - step 2943: {'train/loss': 0.009916938841342926, 'train/grad_norm': 0.7001467943191528, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:51:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:51:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:10] train_sft.py:596 - step 2944: {'train/loss': 0.007172808051109314, 'train/grad_norm': 0.30164238810539246, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 15:51:10] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:51:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:15] train_sft.py:596 - step 2945: {'train/loss': 0.008516594767570496, 'train/grad_norm': 0.20000097155570984, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:51:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:51:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:20] train_sft.py:596 - step 2946: {'train/loss': 0.010199889540672302, 'train/grad_norm': 0.3720006048679352, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:51:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:51:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:24] train_sft.py:596 - step 2947: {'train/loss': 0.011472448706626892, 'train/grad_norm': 0.4207903742790222, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:51:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:51:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:29] train_sft.py:596 - step 2948: {'train/loss': 0.013930201530456543, 'train/grad_norm': 0.5495060086250305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 15:51:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:51:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:34] train_sft.py:596 - step 2949: {'train/loss': 0.012689946219325066, 'train/grad_norm': 0.5076931715011597, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:51:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:51:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:38] train_sft.py:596 - step 2950: {'train/loss': 0.012536883354187012, 'train/grad_norm': 0.3467520475387573, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:51:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:51:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:43] train_sft.py:596 - step 2951: {'train/loss': 0.016316644847393036, 'train/grad_norm': 1.0194456577301025, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:51:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:51:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:48] train_sft.py:596 - step 2952: {'train/loss': 0.017115868628025055, 'train/grad_norm': 0.36285778880119324, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:51:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:51:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:53] train_sft.py:596 - step 2953: {'train/loss': 0.015009909868240356, 'train/grad_norm': 0.30855146050453186, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:51:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:51:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:51:57] train_sft.py:596 - step 2954: {'train/loss': 0.011800698935985565, 'train/grad_norm': 0.4538581371307373, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:51:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:51:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:02] train_sft.py:596 - step 2955: {'train/loss': 0.012616120278835297, 'train/grad_norm': 0.3324483633041382, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:52:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:52:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:07] train_sft.py:596 - step 2956: {'train/loss': 0.01041257381439209, 'train/grad_norm': 0.4044831395149231, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 15:52:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:52:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:11] train_sft.py:596 - step 2957: {'train/loss': 0.01418951153755188, 'train/grad_norm': 0.48823392391204834, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:52:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:52:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:16] train_sft.py:596 - step 2958: {'train/loss': 0.01427072286605835, 'train/grad_norm': 0.3429073095321655, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:52:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:52:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:21] train_sft.py:596 - step 2959: {'train/loss': 0.012970034033060074, 'train/grad_norm': 0.43024587631225586, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:52:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:52:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:25] train_sft.py:596 - step 2960: {'train/loss': 0.010188944637775421, 'train/grad_norm': 0.2920548617839813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 15:52:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:52:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:30] train_sft.py:596 - step 2961: {'train/loss': 0.01591840572655201, 'train/grad_norm': 0.34524208307266235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:52:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:52:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:35] train_sft.py:596 - step 2962: {'train/loss': 0.013124644756317139, 'train/grad_norm': 0.4645509421825409, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:52:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:52:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:39] train_sft.py:596 - step 2963: {'train/loss': 0.011997321154922247, 'train/grad_norm': 0.32237982749938965, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:52:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:52:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:45] train_sft.py:596 - step 2964: {'train/loss': 0.015173003077507019, 'train/grad_norm': 0.3241691291332245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 15:52:45] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:52:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:49] train_sft.py:596 - step 2965: {'train/loss': 0.016744986176490784, 'train/grad_norm': 0.4049447178840637, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 15:52:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:52:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:54] train_sft.py:596 - step 2966: {'train/loss': 0.011555135250091553, 'train/grad_norm': 0.3802570700645447, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:52:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:52:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:52:58] train_sft.py:596 - step 2967: {'train/loss': 0.012869402766227722, 'train/grad_norm': 0.291797012090683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:52:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:52:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:03] train_sft.py:596 - step 2968: {'train/loss': 0.006362998858094215, 'train/grad_norm': 0.1899074912071228, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 15:53:03] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:53:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:08] train_sft.py:596 - step 2969: {'train/loss': 0.01560521125793457, 'train/grad_norm': 0.4947395920753479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:53:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:53:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:13] train_sft.py:596 - step 2970: {'train/loss': 0.008099749684333801, 'train/grad_norm': 0.3034025728702545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 15:53:13] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:53:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:17] train_sft.py:596 - step 2971: {'train/loss': 0.013373509049415588, 'train/grad_norm': 0.3971562683582306, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 15:53:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:53:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:22] train_sft.py:596 - step 2972: {'train/loss': 0.016229093074798584, 'train/grad_norm': 0.3586956858634949, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 15:53:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:53:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:27] train_sft.py:596 - step 2973: {'train/loss': 0.011163376271724701, 'train/grad_norm': 0.3474034368991852, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 15:53:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:53:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:31] train_sft.py:596 - step 2974: {'train/loss': 0.01488734781742096, 'train/grad_norm': 0.3380749225616455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:53:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:53:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:36] train_sft.py:596 - step 2975: {'train/loss': 0.012399513274431229, 'train/grad_norm': 0.2936152219772339, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 15:53:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:53:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:41] train_sft.py:596 - step 2976: {'train/loss': 0.01142331026494503, 'train/grad_norm': 0.49217620491981506, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 15:53:41] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:53:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:46] train_sft.py:596 - step 2977: {'train/loss': 0.014860302209854126, 'train/grad_norm': 0.42058300971984863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:53:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:53:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:51] train_sft.py:596 - step 2978: {'train/loss': 0.01252003014087677, 'train/grad_norm': 0.3564552068710327, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 15:53:51] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:53:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:53:56] train_sft.py:596 - step 2979: {'train/loss': 0.00977209210395813, 'train/grad_norm': 0.2943404018878937, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 15:53:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:53:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:00] train_sft.py:596 - step 2980: {'train/loss': 0.02157685160636902, 'train/grad_norm': 0.4515833556652069, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 15:54:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:54:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:05] train_sft.py:596 - step 2981: {'train/loss': 0.011041060090065002, 'train/grad_norm': 0.3289489448070526, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 15:54:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:54:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:10] train_sft.py:596 - step 2982: {'train/loss': 0.013324867933988571, 'train/grad_norm': 0.5849157571792603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:54:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:54:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:14] train_sft.py:596 - step 2983: {'train/loss': 0.0122661292552948, 'train/grad_norm': 0.34982314705848694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:54:14] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:54:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:20] train_sft.py:596 - step 2984: {'train/loss': 0.015329994261264801, 'train/grad_norm': 0.45303064584732056, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
[2025-12-29 15:54:20] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 15:54:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:25] train_sft.py:596 - step 2985: {'train/loss': 0.012141704559326172, 'train/grad_norm': 0.19804821908473969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 15:54:25] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:54:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:30] train_sft.py:596 - step 2986: {'train/loss': 0.019235312938690186, 'train/grad_norm': 0.6215894818305969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 15:54:30] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 15:54:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:34] train_sft.py:596 - step 2987: {'train/loss': 0.021260235458612442, 'train/grad_norm': 0.5903578996658325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 15:54:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:54:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:39] train_sft.py:596 - step 2988: {'train/loss': 0.020532429218292236, 'train/grad_norm': 0.46330443024635315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 15:54:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:54:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:44] train_sft.py:596 - step 2989: {'train/loss': 0.012854505330324173, 'train/grad_norm': 0.4451327323913574, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 15:54:44] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:54:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:49] train_sft.py:596 - step 2990: {'train/loss': 0.015513673424720764, 'train/grad_norm': 0.4516083598136902, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 15:54:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:54:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:54] train_sft.py:596 - step 2991: {'train/loss': 0.02088436484336853, 'train/grad_norm': 0.4965744614601135, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:54:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:54:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:54:58] train_sft.py:596 - step 2992: {'train/loss': 0.010312139987945557, 'train/grad_norm': 0.21607060730457306, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:54:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:54:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:03] train_sft.py:596 - step 2993: {'train/loss': 0.016212791204452515, 'train/grad_norm': 0.32320666313171387, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:55:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:55:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:08] train_sft.py:596 - step 2994: {'train/loss': 0.012635551393032074, 'train/grad_norm': 0.5669060349464417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 15:55:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:55:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:13] train_sft.py:596 - step 2995: {'train/loss': 0.012658372521400452, 'train/grad_norm': 0.3246839642524719, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 15:55:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:55:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:17] train_sft.py:596 - step 2996: {'train/loss': 0.016594771295785904, 'train/grad_norm': 0.3766029477119446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:55:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:55:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:22] train_sft.py:596 - step 2997: {'train/loss': 0.015597015619277954, 'train/grad_norm': 0.6653008460998535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 15:55:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:55:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:26] train_sft.py:596 - step 2998: {'train/loss': 0.01578676700592041, 'train/grad_norm': 0.40046972036361694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:55:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:55:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:55:31] train_sft.py:596 - step 2999: {'train/loss': 0.014762293547391891, 'train/grad_norm': 0.3531661033630371, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:55:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:55:43] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0003000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:02<04:42,  2.85s/it]
actor_val:   2%|▏         | 2/100 [00:04<03:42,  2.27s/it]
actor_val:   3%|▎         | 3/100 [00:06<03:09,  1.95s/it]
actor_val:   4%|▍         | 4/100 [00:07<02:51,  1.78s/it]
actor_val:   5%|▌         | 5/100 [00:09<02:41,  1.70s/it]
actor_val:   6%|▌         | 6/100 [00:10<02:34,  1.64s/it]
actor_val:   7%|▋         | 7/100 [00:12<02:30,  1.62s/it]
actor_val:   8%|▊         | 8/100 [00:13<02:26,  1.59s/it]
actor_val:   9%|▉         | 9/100 [00:15<02:24,  1.58s/it]
actor_val:  10%|█         | 10/100 [00:17<02:20,  1.56s/it]
actor_val:  11%|█         | 11/100 [00:18<02:18,  1.55s/it]
actor_val:  12%|█▏        | 12/100 [00:20<02:16,  1.56s/it]
actor_val:  13%|█▎        | 13/100 [00:21<02:15,  1.55s/it]
actor_val:  14%|█▍        | 14/100 [00:23<02:12,  1.54s/it]
actor_val:  15%|█▌        | 15/100 [00:25<02:18,  1.63s/it]
actor_val:  16%|█▌        | 16/100 [00:26<02:21,  1.69s/it]
actor_val:  17%|█▋        | 17/100 [00:28<02:16,  1.65s/it]
actor_val:  18%|█▊        | 18/100 [00:30<02:18,  1.69s/it]
actor_val:  19%|█▉        | 19/100 [00:32<02:25,  1.80s/it]
actor_val:  20%|██        | 20/100 [00:33<02:17,  1.72s/it]
actor_val:  21%|██        | 21/100 [00:35<02:17,  1.73s/it]
actor_val:  22%|██▏       | 22/100 [00:37<02:16,  1.75s/it]
actor_val:  23%|██▎       | 23/100 [00:38<02:09,  1.69s/it]
actor_val:  24%|██▍       | 24/100 [00:40<02:04,  1.64s/it]
actor_val:  25%|██▌       | 25/100 [00:42<02:01,  1.61s/it]
actor_val:  26%|██▌       | 26/100 [00:43<02:05,  1.69s/it]
actor_val:  27%|██▋       | 27/100 [00:45<02:00,  1.65s/it]
actor_val:  28%|██▊       | 28/100 [00:47<02:02,  1.70s/it]
actor_val:  29%|██▉       | 29/100 [00:48<01:57,  1.66s/it]
actor_val:  30%|███       | 30/100 [00:50<01:53,  1.63s/it]
actor_val:  31%|███       | 31/100 [00:51<01:51,  1.62s/it]
actor_val:  32%|███▏      | 32/100 [00:53<01:47,  1.59s/it]
actor_val:  33%|███▎      | 33/100 [00:55<01:45,  1.58s/it]
actor_val:  34%|███▍      | 34/100 [00:56<01:49,  1.66s/it]
actor_val:  35%|███▌      | 35/100 [00:58<01:50,  1.70s/it]
actor_val:  36%|███▌      | 36/100 [01:00<01:45,  1.65s/it]
actor_val:  37%|███▋      | 37/100 [01:02<01:47,  1.70s/it]
actor_val:  38%|███▊      | 38/100 [01:03<01:43,  1.66s/it]
actor_val:  39%|███▉      | 39/100 [01:05<01:39,  1.62s/it]
actor_val:  40%|████      | 40/100 [01:06<01:36,  1.61s/it]
actor_val:  41%|████      | 41/100 [01:08<01:38,  1.67s/it]
actor_val:  42%|████▏     | 42/100 [01:10<01:34,  1.64s/it]
actor_val:  43%|████▎     | 43/100 [01:11<01:31,  1.60s/it]
actor_val:  44%|████▍     | 44/100 [01:13<01:33,  1.67s/it]
actor_val:  45%|████▌     | 45/100 [01:14<01:29,  1.63s/it]
actor_val:  46%|████▌     | 46/100 [01:16<01:27,  1.62s/it]
actor_val:  47%|████▋     | 47/100 [01:18<01:28,  1.68s/it]
actor_val:  48%|████▊     | 48/100 [01:19<01:25,  1.64s/it]
actor_val:  49%|████▉     | 49/100 [01:21<01:22,  1.61s/it]
actor_val:  50%|█████     | 50/100 [01:23<01:19,  1.60s/it]
actor_val:  51%|█████     | 51/100 [01:24<01:18,  1.60s/it]
actor_val:  52%|█████▏    | 52/100 [01:26<01:19,  1.66s/it]
actor_val:  53%|█████▎    | 53/100 [01:27<01:16,  1.62s/it]
actor_val:  54%|█████▍    | 54/100 [01:29<01:17,  1.68s/it]
actor_val:  55%|█████▌    | 55/100 [01:31<01:13,  1.64s/it]
actor_val:  56%|█████▌    | 56/100 [01:33<01:14,  1.70s/it]
actor_val:  57%|█████▋    | 57/100 [01:34<01:13,  1.72s/it]
actor_val:  58%|█████▊    | 58/100 [01:36<01:13,  1.75s/it]
actor_val:  59%|█████▉    | 59/100 [01:38<01:09,  1.69s/it]
actor_val:  60%|██████    | 60/100 [01:39<01:05,  1.64s/it]
actor_val:  61%|██████    | 61/100 [01:41<01:03,  1.62s/it]
actor_val:  62%|██████▏   | 62/100 [01:42<01:00,  1.59s/it]
actor_val:  63%|██████▎   | 63/100 [01:44<00:58,  1.58s/it]
actor_val:  64%|██████▍   | 64/100 [01:46<00:57,  1.59s/it]
actor_val:  65%|██████▌   | 65/100 [01:47<00:55,  1.57s/it]
actor_val:  66%|██████▌   | 66/100 [01:49<00:53,  1.58s/it]
actor_val:  67%|██████▋   | 67/100 [01:50<00:51,  1.57s/it]
actor_val:  68%|██████▊   | 68/100 [01:52<00:50,  1.56s/it]
actor_val:  69%|██████▉   | 69/100 [01:54<00:50,  1.64s/it]
actor_val:  70%|███████   | 70/100 [01:55<00:48,  1.60s/it]
actor_val:  71%|███████   | 71/100 [01:57<00:46,  1.59s/it]
actor_val:  72%|███████▏  | 72/100 [01:59<00:46,  1.67s/it]
actor_val:  73%|███████▎  | 73/100 [02:00<00:44,  1.63s/it]
actor_val:  74%|███████▍  | 74/100 [02:02<00:42,  1.62s/it]
actor_val:  75%|███████▌  | 75/100 [02:03<00:41,  1.66s/it]
actor_val:  76%|███████▌  | 76/100 [02:05<00:41,  1.73s/it]
actor_val:  77%|███████▋  | 77/100 [02:07<00:38,  1.67s/it]
actor_val:  78%|███████▊  | 78/100 [02:08<00:36,  1.64s/it]
actor_val:  79%|███████▉  | 79/100 [02:10<00:35,  1.68s/it]
actor_val:  80%|████████  | 80/100 [02:12<00:34,  1.72s/it]
actor_val:  81%|████████  | 81/100 [02:14<00:33,  1.74s/it]
actor_val:  82%|████████▏ | 82/100 [02:15<00:30,  1.68s/it]
actor_val:  83%|████████▎ | 83/100 [02:17<00:27,  1.64s/it]
actor_val:  84%|████████▍ | 84/100 [02:19<00:26,  1.68s/it]
actor_val:  85%|████████▌ | 85/100 [02:20<00:24,  1.65s/it]
actor_val:  86%|████████▌ | 86/100 [02:22<00:22,  1.61s/it]
actor_val:  87%|████████▋ | 87/100 [02:23<00:20,  1.59s/it]
actor_val:  88%|████████▊ | 88/100 [02:25<00:18,  1.57s/it]
actor_val:  89%|████████▉ | 89/100 [02:27<00:17,  1.63s/it]
actor_val:  90%|█████████ | 90/100 [02:28<00:16,  1.62s/it]
actor_val:  91%|█████████ | 91/100 [02:30<00:15,  1.67s/it]
actor_val:  92%|█████████▏| 92/100 [02:32<00:13,  1.64s/it]
actor_val:  93%|█████████▎| 93/100 [02:33<00:11,  1.68s/it]
actor_val:  94%|█████████▍| 94/100 [02:35<00:09,  1.64s/it]
actor_val:  95%|█████████▌| 95/100 [02:37<00:08,  1.71s/it]
actor_val:  96%|█████████▌| 96/100 [02:38<00:06,  1.66s/it]
actor_val:  97%|█████████▋| 97/100 [02:40<00:05,  1.69s/it]
actor_val:  98%|█████████▊| 98/100 [02:42<00:03,  1.65s/it]
actor_val:  99%|█████████▉| 99/100 [02:43<00:01,  1.61s/it]
actor_val: 100%|██████████| 100/100 [02:45<00:00,  1.69s/it]
actor_val: 100%|██████████| 100/100 [02:45<00:00,  1.66s/it]
[2025-12-29 15:58:29] train_sft.py:658 - step 3000: {'val/loss': 0.46859375}
[2025-12-29 15:58:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:58:35] train_sft.py:596 - step 3000: {'train/loss': 0.012873165309429169, 'train/grad_norm': 0.5690664052963257, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.09s/it]
[2025-12-29 15:58:35] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 15:58:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:58:39] train_sft.py:596 - step 3001: {'train/loss': 0.016853932291269302, 'train/grad_norm': 0.5569263696670532, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:58:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:58:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:58:44] train_sft.py:596 - step 3002: {'train/loss': 0.010450907051563263, 'train/grad_norm': 0.3261265456676483, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:58:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:58:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:58:49] train_sft.py:596 - step 3003: {'train/loss': 0.01634317636489868, 'train/grad_norm': 0.4540036618709564, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 15:58:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:58:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:58:54] train_sft.py:596 - step 3004: {'train/loss': 0.012821346521377563, 'train/grad_norm': 0.40677011013031006, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 15:58:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:58:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:58:59] train_sft.py:596 - step 3005: {'train/loss': 0.009312435984611511, 'train/grad_norm': 0.4042930603027344, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 15:58:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:58:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:03] train_sft.py:596 - step 3006: {'train/loss': 0.011163882911205292, 'train/grad_norm': 0.36900508403778076, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:59:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:59:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:08] train_sft.py:596 - step 3007: {'train/loss': 0.011850833892822266, 'train/grad_norm': 0.3138688802719116, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 15:59:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 15:59:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:13] train_sft.py:596 - step 3008: {'train/loss': 0.012333676218986511, 'train/grad_norm': 0.4362507462501526, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 15:59:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:59:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:18] train_sft.py:596 - step 3009: {'train/loss': 0.02179262414574623, 'train/grad_norm': 0.46582362055778503, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 15:59:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:59:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:23] train_sft.py:596 - step 3010: {'train/loss': 0.01789472997188568, 'train/grad_norm': 0.8204490542411804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 15:59:23] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 15:59:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:28] train_sft.py:596 - step 3011: {'train/loss': 0.012180179357528687, 'train/grad_norm': 0.524608850479126, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 15:59:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:59:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:32] train_sft.py:596 - step 3012: {'train/loss': 0.013564512133598328, 'train/grad_norm': 0.42991307377815247, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 15:59:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:59:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:37] train_sft.py:596 - step 3013: {'train/loss': 0.014063046779483557, 'train/grad_norm': 0.4485396444797516, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 15:59:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 15:59:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:41] train_sft.py:596 - step 3014: {'train/loss': 0.022381462156772614, 'train/grad_norm': 0.5069738626480103, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 15:59:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:59:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:46] train_sft.py:596 - step 3015: {'train/loss': 0.01439542113803327, 'train/grad_norm': 0.35721316933631897, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 15:59:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 15:59:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:51] train_sft.py:596 - step 3016: {'train/loss': 0.010855913162231445, 'train/grad_norm': 0.44586142897605896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 15:59:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 15:59:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 15:59:56] train_sft.py:596 - step 3017: {'train/loss': 0.017134785652160645, 'train/grad_norm': 0.6048828363418579, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 15:59:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 15:59:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:00] train_sft.py:596 - step 3018: {'train/loss': 0.013768285512924194, 'train/grad_norm': 0.4168832302093506, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:00:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:00:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:05] train_sft.py:596 - step 3019: {'train/loss': 0.02228417992591858, 'train/grad_norm': 0.5152475833892822, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:00:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:00:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:10] train_sft.py:596 - step 3020: {'train/loss': 0.022244930267333984, 'train/grad_norm': 0.6638335585594177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:00:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:00:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:15] train_sft.py:596 - step 3021: {'train/loss': 0.020014621317386627, 'train/grad_norm': 0.39684024453163147, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 16:00:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:00:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:19] train_sft.py:596 - step 3022: {'train/loss': 0.016747310757637024, 'train/grad_norm': 0.5934529900550842, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:00:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:00:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:24] train_sft.py:596 - step 3023: {'train/loss': 0.014371119439601898, 'train/grad_norm': 0.4068739712238312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 16:00:24] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:00:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:29] train_sft.py:596 - step 3024: {'train/loss': 0.009451054036617279, 'train/grad_norm': 0.29898810386657715, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:00:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:00:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:34] train_sft.py:596 - step 3025: {'train/loss': 0.015832915902137756, 'train/grad_norm': 0.27310439944267273, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 16:00:34] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:00:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:39] train_sft.py:596 - step 3026: {'train/loss': 0.016410306096076965, 'train/grad_norm': 0.35508933663368225, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:00:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:00:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:44] train_sft.py:596 - step 3027: {'train/loss': 0.012551158666610718, 'train/grad_norm': 0.31943970918655396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:00:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:00:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:49] train_sft.py:596 - step 3028: {'train/loss': 0.012170553207397461, 'train/grad_norm': 0.474687784910202, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:00:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:00:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:53] train_sft.py:596 - step 3029: {'train/loss': 0.011330537497997284, 'train/grad_norm': 0.37835633754730225, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:00:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:00:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:00:58] train_sft.py:596 - step 3030: {'train/loss': 0.01282760500907898, 'train/grad_norm': 0.29289016127586365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:00:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:00:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:03] train_sft.py:596 - step 3031: {'train/loss': 0.017342329025268555, 'train/grad_norm': 0.4178696572780609, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:01:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:01:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:07] train_sft.py:596 - step 3032: {'train/loss': 0.015072796493768692, 'train/grad_norm': 0.37427738308906555, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:01:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:01:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:12] train_sft.py:596 - step 3033: {'train/loss': 0.009856745600700378, 'train/grad_norm': 0.377418577671051, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:01:12] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:01:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:17] train_sft.py:596 - step 3034: {'train/loss': 0.016642674803733826, 'train/grad_norm': 0.5145500898361206, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:01:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:01:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:22] train_sft.py:596 - step 3035: {'train/loss': 0.01901976764202118, 'train/grad_norm': 0.5610897541046143, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:01:22] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:01:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:27] train_sft.py:596 - step 3036: {'train/loss': 0.013016728800721467, 'train/grad_norm': 0.491300106048584, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:01:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:01:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:32] train_sft.py:596 - step 3037: {'train/loss': 0.014200009405612946, 'train/grad_norm': 0.4852295517921448, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:01:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:01:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:36] train_sft.py:596 - step 3038: {'train/loss': 0.013620689511299133, 'train/grad_norm': 0.5435104966163635, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:01:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:01:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:41] train_sft.py:596 - step 3039: {'train/loss': 0.017129838466644287, 'train/grad_norm': 0.4006470739841461, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:01:41] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:01:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:46] train_sft.py:596 - step 3040: {'train/loss': 0.016197457909584045, 'train/grad_norm': 0.39590558409690857, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 16:01:46] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:01:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:51] train_sft.py:596 - step 3041: {'train/loss': 0.01989557221531868, 'train/grad_norm': 0.6438342928886414, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:01:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:01:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:01:55] train_sft.py:596 - step 3042: {'train/loss': 0.009137250483036041, 'train/grad_norm': 0.2344771772623062, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:01:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:01:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:00] train_sft.py:596 - step 3043: {'train/loss': 0.019383899867534637, 'train/grad_norm': 0.34408122301101685, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:02:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:02:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:05] train_sft.py:596 - step 3044: {'train/loss': 0.021197475492954254, 'train/grad_norm': 0.532853364944458, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 16:02:05] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:02:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:10] train_sft.py:596 - step 3045: {'train/loss': 0.015539534389972687, 'train/grad_norm': 0.5364041328430176, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 16:02:10] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:02:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:15] train_sft.py:596 - step 3046: {'train/loss': 0.017961825244128704, 'train/grad_norm': 0.5236226916313171, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:02:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:02:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:20] train_sft.py:596 - step 3047: {'train/loss': 0.016266100108623505, 'train/grad_norm': 0.3834240734577179, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:02:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:02:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:24] train_sft.py:596 - step 3048: {'train/loss': 0.011181473731994629, 'train/grad_norm': 0.35656917095184326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:02:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:02:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:29] train_sft.py:596 - step 3049: {'train/loss': 0.009665101766586304, 'train/grad_norm': 0.31553521752357483, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:02:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:02:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:34] train_sft.py:596 - step 3050: {'train/loss': 0.01731663942337036, 'train/grad_norm': 0.39705440402030945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:02:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:02:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:39] train_sft.py:596 - step 3051: {'train/loss': 0.024692371487617493, 'train/grad_norm': 0.30578145384788513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:02:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:02:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:44] train_sft.py:596 - step 3052: {'train/loss': 0.017363503575325012, 'train/grad_norm': 0.4214365780353546, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:02:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:02:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:49] train_sft.py:596 - step 3053: {'train/loss': 0.014658600091934204, 'train/grad_norm': 0.49254298210144043, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:02:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:02:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:53] train_sft.py:596 - step 3054: {'train/loss': 0.012722905725240707, 'train/grad_norm': 0.6086211800575256, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:02:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:02:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:02:58] train_sft.py:596 - step 3055: {'train/loss': 0.009622901678085327, 'train/grad_norm': 0.3341244161128998, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:02:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:02:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:03] train_sft.py:596 - step 3056: {'train/loss': 0.014196962118148804, 'train/grad_norm': 0.4533144533634186, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:03:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:03:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:07] train_sft.py:596 - step 3057: {'train/loss': 0.013897895812988281, 'train/grad_norm': 0.4888657033443451, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:03:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:03:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:12] train_sft.py:596 - step 3058: {'train/loss': 0.011499926447868347, 'train/grad_norm': 0.41144588589668274, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:03:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:03:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:17] train_sft.py:596 - step 3059: {'train/loss': 0.021061241626739502, 'train/grad_norm': 0.5215831995010376, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:03:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:03:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:21] train_sft.py:596 - step 3060: {'train/loss': 0.01510167121887207, 'train/grad_norm': 0.25424399971961975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:03:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:03:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:26] train_sft.py:596 - step 3061: {'train/loss': 0.017650514841079712, 'train/grad_norm': 0.29902181029319763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:03:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:03:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:30] train_sft.py:596 - step 3062: {'train/loss': 0.01826205849647522, 'train/grad_norm': 0.3641659617424011, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:03:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:03:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:35] train_sft.py:596 - step 3063: {'train/loss': 0.008717060089111328, 'train/grad_norm': 0.16459763050079346, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:03:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:03:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:40] train_sft.py:596 - step 3064: {'train/loss': 0.01760151982307434, 'train/grad_norm': 0.3971154987812042, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:03:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:03:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:44] train_sft.py:596 - step 3065: {'train/loss': 0.010404765605926514, 'train/grad_norm': 0.42631205916404724, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:03:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:03:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:49] train_sft.py:596 - step 3066: {'train/loss': 0.00822882354259491, 'train/grad_norm': 0.2450592815876007, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:03:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:03:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:54] train_sft.py:596 - step 3067: {'train/loss': 0.01411968469619751, 'train/grad_norm': 0.4088553786277771, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:03:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:03:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:03:59] train_sft.py:596 - step 3068: {'train/loss': 0.017541438341140747, 'train/grad_norm': 0.49323344230651855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:03:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:03:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:03] train_sft.py:596 - step 3069: {'train/loss': 0.0197279155254364, 'train/grad_norm': 0.6938850283622742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:04:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:04:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:08] train_sft.py:596 - step 3070: {'train/loss': 0.016714945435523987, 'train/grad_norm': 0.5878023505210876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:04:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:04:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:13] train_sft.py:596 - step 3071: {'train/loss': 0.019650518894195557, 'train/grad_norm': 0.4847089350223541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:04:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:04:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:18] train_sft.py:596 - step 3072: {'train/loss': 0.014565348625183105, 'train/grad_norm': 0.33444342017173767, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:04:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:04:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:22] train_sft.py:596 - step 3073: {'train/loss': 0.010960817337036133, 'train/grad_norm': 0.34959790110588074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:04:22] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:04:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:27] train_sft.py:596 - step 3074: {'train/loss': 0.015848252922296524, 'train/grad_norm': 0.4137328565120697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:04:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:04:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:32] train_sft.py:596 - step 3075: {'train/loss': 0.01209692656993866, 'train/grad_norm': 0.32139813899993896, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:04:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:04:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:37] train_sft.py:596 - step 3076: {'train/loss': 0.007892414927482605, 'train/grad_norm': 0.3248981535434723, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:04:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:04:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:42] train_sft.py:596 - step 3077: {'train/loss': 0.022666141390800476, 'train/grad_norm': 0.5869214534759521, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:04:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:04:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:46] train_sft.py:596 - step 3078: {'train/loss': 0.014729678630828857, 'train/grad_norm': 0.5271387696266174, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:04:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:04:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:51] train_sft.py:596 - step 3079: {'train/loss': 0.010810963809490204, 'train/grad_norm': 0.3305307924747467, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:04:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:04:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:04:55] train_sft.py:596 - step 3080: {'train/loss': 0.016088545322418213, 'train/grad_norm': 0.34522584080696106, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:04:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:04:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:00] train_sft.py:596 - step 3081: {'train/loss': 0.017827719449996948, 'train/grad_norm': 0.4610287845134735, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:05:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:05:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:05] train_sft.py:596 - step 3082: {'train/loss': 0.012723684310913086, 'train/grad_norm': 0.36171209812164307, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:05:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:05:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:10] train_sft.py:596 - step 3083: {'train/loss': 0.014433290809392929, 'train/grad_norm': 0.3340308666229248, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:05:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:05:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:14] train_sft.py:596 - step 3084: {'train/loss': 0.012279905378818512, 'train/grad_norm': 0.30308645963668823, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:05:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:05:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:19] train_sft.py:596 - step 3085: {'train/loss': 0.014702748507261276, 'train/grad_norm': 0.5211105346679688, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:05:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:05:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:24] train_sft.py:596 - step 3086: {'train/loss': 0.014018900692462921, 'train/grad_norm': 0.47560736536979675, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:05:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:05:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:29] train_sft.py:596 - step 3087: {'train/loss': 0.013373583555221558, 'train/grad_norm': 0.44071686267852783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:05:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:05:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:33] train_sft.py:596 - step 3088: {'train/loss': 0.014209061861038208, 'train/grad_norm': 0.40477415919303894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:05:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:05:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:39] train_sft.py:596 - step 3089: {'train/loss': 0.01585695706307888, 'train/grad_norm': 0.5006411671638489, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 16:05:39] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:05:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:44] train_sft.py:596 - step 3090: {'train/loss': 0.014378000050783157, 'train/grad_norm': 0.5420737266540527, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:05:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:05:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:48] train_sft.py:596 - step 3091: {'train/loss': 0.022049017250537872, 'train/grad_norm': 0.6251034140586853, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:05:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:05:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:53] train_sft.py:596 - step 3092: {'train/loss': 0.01255810260772705, 'train/grad_norm': 0.5685529708862305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:05:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:05:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:05:58] train_sft.py:596 - step 3093: {'train/loss': 0.009039804339408875, 'train/grad_norm': 0.4493027627468109, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:05:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:05:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:03] train_sft.py:596 - step 3094: {'train/loss': 0.011573031544685364, 'train/grad_norm': 0.2694193720817566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:06:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:06:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:07] train_sft.py:596 - step 3095: {'train/loss': 0.011536668986082077, 'train/grad_norm': 0.4655627906322479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:06:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:06:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:12] train_sft.py:596 - step 3096: {'train/loss': 0.020595505833625793, 'train/grad_norm': 0.4290643632411957, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:06:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:06:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:17] train_sft.py:596 - step 3097: {'train/loss': 0.015524864196777344, 'train/grad_norm': 0.7449448108673096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:06:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:06:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:22] train_sft.py:596 - step 3098: {'train/loss': 0.013695351779460907, 'train/grad_norm': 0.4142647981643677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 16:06:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:06:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:26] train_sft.py:596 - step 3099: {'train/loss': 0.018853576853871346, 'train/grad_norm': 0.4097885489463806, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:06:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:06:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:31] train_sft.py:596 - step 3100: {'train/loss': 0.014549784362316132, 'train/grad_norm': 0.4538525938987732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:06:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:06:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:36] train_sft.py:596 - step 3101: {'train/loss': 0.010043323040008545, 'train/grad_norm': 0.24821224808692932, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:06:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:06:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:41] train_sft.py:596 - step 3102: {'train/loss': 0.011381223797798157, 'train/grad_norm': 0.4489627182483673, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:06:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:06:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:45] train_sft.py:596 - step 3103: {'train/loss': 0.02053999900817871, 'train/grad_norm': 0.6327049136161804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:06:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:06:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:50] train_sft.py:596 - step 3104: {'train/loss': 0.013558179140090942, 'train/grad_norm': 0.529897153377533, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:06:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:06:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:06:54] train_sft.py:596 - step 3105: {'train/loss': 0.01462797075510025, 'train/grad_norm': 0.43223243951797485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:06:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:06:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:00] train_sft.py:596 - step 3106: {'train/loss': 0.02011089026927948, 'train/grad_norm': 0.7923390865325928, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 16:07:00] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:07:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:04] train_sft.py:596 - step 3107: {'train/loss': 0.017697498202323914, 'train/grad_norm': 0.6155385971069336, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:07:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:07:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:09] train_sft.py:596 - step 3108: {'train/loss': 0.02427348494529724, 'train/grad_norm': 0.5920630693435669, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:07:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:07:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:14] train_sft.py:596 - step 3109: {'train/loss': 0.019087255001068115, 'train/grad_norm': 0.4432034194469452, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:07:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:07:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:18] train_sft.py:596 - step 3110: {'train/loss': 0.012413417920470238, 'train/grad_norm': 0.34630852937698364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:07:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:07:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:23] train_sft.py:596 - step 3111: {'train/loss': 0.01072201132774353, 'train/grad_norm': 0.3974256217479706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:07:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:07:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:28] train_sft.py:596 - step 3112: {'train/loss': 0.030378766357898712, 'train/grad_norm': 0.7536365389823914, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:07:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:07:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:32] train_sft.py:596 - step 3113: {'train/loss': 0.026420608162879944, 'train/grad_norm': 0.5145053863525391, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:07:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:07:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:37] train_sft.py:596 - step 3114: {'train/loss': 0.02036125957965851, 'train/grad_norm': 0.6015377044677734, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:07:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:07:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:42] train_sft.py:596 - step 3115: {'train/loss': 0.025847792625427246, 'train/grad_norm': 0.4781275689601898, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:07:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:07:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:46] train_sft.py:596 - step 3116: {'train/loss': 0.01821470633149147, 'train/grad_norm': 0.5932741761207581, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:07:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:07:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:51] train_sft.py:596 - step 3117: {'train/loss': 0.014252394437789917, 'train/grad_norm': 0.3151024281978607, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:07:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:07:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:07:56] train_sft.py:596 - step 3118: {'train/loss': 0.02145165205001831, 'train/grad_norm': 0.5849111080169678, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:07:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:07:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:01] train_sft.py:596 - step 3119: {'train/loss': 0.015664398670196533, 'train/grad_norm': 0.41311565041542053, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:08:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:08:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:05] train_sft.py:596 - step 3120: {'train/loss': 0.016368359327316284, 'train/grad_norm': 0.5019758343696594, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:08:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:08:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:10] train_sft.py:596 - step 3121: {'train/loss': 0.01876235008239746, 'train/grad_norm': 0.7857099771499634, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:08:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:08:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:14] train_sft.py:596 - step 3122: {'train/loss': 0.01109786331653595, 'train/grad_norm': 0.4168427884578705, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:08:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:08:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:19] train_sft.py:596 - step 3123: {'train/loss': 0.0165712833404541, 'train/grad_norm': 0.3320123255252838, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:08:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:08:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:24] train_sft.py:596 - step 3124: {'train/loss': 0.01803535223007202, 'train/grad_norm': 0.556003212928772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:08:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:08:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:29] train_sft.py:596 - step 3125: {'train/loss': 0.013608992099761963, 'train/grad_norm': 0.3281184434890747, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:08:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:08:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:33] train_sft.py:596 - step 3126: {'train/loss': 0.007280901074409485, 'train/grad_norm': 0.25627991557121277, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:08:33] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:08:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:38] train_sft.py:596 - step 3127: {'train/loss': 0.011762499809265137, 'train/grad_norm': 0.37391871213912964, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:08:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:08:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:43] train_sft.py:596 - step 3128: {'train/loss': 0.011901140213012695, 'train/grad_norm': 0.31176862120628357, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:08:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:08:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:47] train_sft.py:596 - step 3129: {'train/loss': 0.01528216153383255, 'train/grad_norm': 0.28077834844589233, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:08:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:08:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:52] train_sft.py:596 - step 3130: {'train/loss': 0.016242802143096924, 'train/grad_norm': 0.3462618887424469, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:08:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:08:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:08:57] train_sft.py:596 - step 3131: {'train/loss': 0.017244726419448853, 'train/grad_norm': 0.3762367367744446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:08:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:08:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:02] train_sft.py:596 - step 3132: {'train/loss': 0.019542016088962555, 'train/grad_norm': 0.4221371114253998, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:09:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:09:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:06] train_sft.py:596 - step 3133: {'train/loss': 0.013648778200149536, 'train/grad_norm': 0.4542962610721588, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:09:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:09:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:11] train_sft.py:596 - step 3134: {'train/loss': 0.014226255938410759, 'train/grad_norm': 0.49463099241256714, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:09:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:09:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:16] train_sft.py:596 - step 3135: {'train/loss': 0.01622992753982544, 'train/grad_norm': 0.46473294496536255, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:09:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:09:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:21] train_sft.py:596 - step 3136: {'train/loss': 0.013220086693763733, 'train/grad_norm': 0.36443716287612915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 16:09:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:09:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:26] train_sft.py:596 - step 3137: {'train/loss': 0.01218780130147934, 'train/grad_norm': 0.3679669201374054, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:09:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:09:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:30] train_sft.py:596 - step 3138: {'train/loss': 0.011519365012645721, 'train/grad_norm': 0.42187753319740295, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:09:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:09:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:35] train_sft.py:596 - step 3139: {'train/loss': 0.017200112342834473, 'train/grad_norm': 0.674582302570343, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:09:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:09:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:40] train_sft.py:596 - step 3140: {'train/loss': 0.019917912781238556, 'train/grad_norm': 0.35830268263816833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:09:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:09:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:45] train_sft.py:596 - step 3141: {'train/loss': 0.018828153610229492, 'train/grad_norm': 0.5965498685836792, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:09:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:09:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:49] train_sft.py:596 - step 3142: {'train/loss': 0.022458772757090628, 'train/grad_norm': 0.5682547092437744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:09:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:09:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:54] train_sft.py:596 - step 3143: {'train/loss': 0.025447227526456118, 'train/grad_norm': 0.8281404972076416, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:09:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:09:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:09:59] train_sft.py:596 - step 3144: {'train/loss': 0.017294976860284805, 'train/grad_norm': 0.39450499415397644, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 16:09:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:09:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:04] train_sft.py:596 - step 3145: {'train/loss': 0.049239542335271835, 'train/grad_norm': 0.431343674659729, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:10:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:10:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:09] train_sft.py:596 - step 3146: {'train/loss': 0.01918230950832367, 'train/grad_norm': 0.649126410484314, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:10:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:10:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:13] train_sft.py:596 - step 3147: {'train/loss': 0.013668596744537354, 'train/grad_norm': 0.2685002386569977, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:10:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:10:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:18] train_sft.py:596 - step 3148: {'train/loss': 0.01811223477125168, 'train/grad_norm': 0.4292314350605011, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:10:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:10:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:23] train_sft.py:596 - step 3149: {'train/loss': 0.019247055053710938, 'train/grad_norm': 0.5498150587081909, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:10:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:10:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:28] train_sft.py:596 - step 3150: {'train/loss': 0.013822793960571289, 'train/grad_norm': 0.48249104619026184, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:10:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:10:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:32] train_sft.py:596 - step 3151: {'train/loss': 0.013781368732452393, 'train/grad_norm': 0.36329519748687744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:10:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:10:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:38] train_sft.py:596 - step 3152: {'train/loss': 0.017567552626132965, 'train/grad_norm': 0.4819307327270508, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
[2025-12-29 16:10:38] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 16:10:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:43] train_sft.py:596 - step 3153: {'train/loss': 0.021479398012161255, 'train/grad_norm': 0.5844590663909912, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 16:10:43] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:10:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:48] train_sft.py:596 - step 3154: {'train/loss': 0.013261448591947556, 'train/grad_norm': 0.5045446753501892, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:10:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:10:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:52] train_sft.py:596 - step 3155: {'train/loss': 0.005791649222373962, 'train/grad_norm': 0.21755221486091614, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:10:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:10:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:10:57] train_sft.py:596 - step 3156: {'train/loss': 0.011023610830307007, 'train/grad_norm': 0.3267173171043396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:10:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:10:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:02] train_sft.py:596 - step 3157: {'train/loss': 0.014679133892059326, 'train/grad_norm': 0.5192418098449707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:11:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:11:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:07] train_sft.py:596 - step 3158: {'train/loss': 0.010364055633544922, 'train/grad_norm': 0.2885178029537201, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:11:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:11:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:11] train_sft.py:596 - step 3159: {'train/loss': 0.011658534407615662, 'train/grad_norm': 0.36766302585601807, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:11:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:11:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:16] train_sft.py:596 - step 3160: {'train/loss': 0.012323372066020966, 'train/grad_norm': 0.45951202511787415, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:11:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:11:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:21] train_sft.py:596 - step 3161: {'train/loss': 0.006180882453918457, 'train/grad_norm': 0.24998162686824799, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:11:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:11:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:26] train_sft.py:596 - step 3162: {'train/loss': 0.011285118758678436, 'train/grad_norm': 0.39081040024757385, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:11:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:11:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:30] train_sft.py:596 - step 3163: {'train/loss': 0.016286693513393402, 'train/grad_norm': 0.394219309091568, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:11:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:11:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:35] train_sft.py:596 - step 3164: {'train/loss': 0.015239417552947998, 'train/grad_norm': 0.4244568645954132, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:11:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:11:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:40] train_sft.py:596 - step 3165: {'train/loss': 0.00976238027215004, 'train/grad_norm': 0.32349199056625366, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:11:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:11:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:45] train_sft.py:596 - step 3166: {'train/loss': 0.012675002217292786, 'train/grad_norm': 0.31280335783958435, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:11:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:11:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:50] train_sft.py:596 - step 3167: {'train/loss': 0.012746575754135847, 'train/grad_norm': 0.4612501263618469, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:11:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:11:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:54] train_sft.py:596 - step 3168: {'train/loss': 0.012017037719488144, 'train/grad_norm': 0.45098432898521423, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:11:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:11:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:11:59] train_sft.py:596 - step 3169: {'train/loss': 0.007061159238219261, 'train/grad_norm': 0.30542775988578796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:11:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:11:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:03] train_sft.py:596 - step 3170: {'train/loss': 0.013722196221351624, 'train/grad_norm': 0.7325513362884521, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:12:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:12:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:08] train_sft.py:596 - step 3171: {'train/loss': 0.014230363070964813, 'train/grad_norm': 0.38092201948165894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 16:12:08] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:12:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:13] train_sft.py:596 - step 3172: {'train/loss': 0.012634612619876862, 'train/grad_norm': 0.4617493748664856, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:12:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:12:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:18] train_sft.py:596 - step 3173: {'train/loss': 0.007874935865402222, 'train/grad_norm': 0.25662994384765625, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 16:12:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:12:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:23] train_sft.py:596 - step 3174: {'train/loss': 0.008116133511066437, 'train/grad_norm': 0.41396403312683105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:12:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:12:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:27] train_sft.py:596 - step 3175: {'train/loss': 0.009484045207500458, 'train/grad_norm': 0.35056233406066895, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:12:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:12:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:32] train_sft.py:596 - step 3176: {'train/loss': 0.010561130940914154, 'train/grad_norm': 0.398567795753479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:12:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:12:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:37] train_sft.py:596 - step 3177: {'train/loss': 0.020495280623435974, 'train/grad_norm': 0.4327934980392456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:12:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:12:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:42] train_sft.py:596 - step 3178: {'train/loss': 0.011902041733264923, 'train/grad_norm': 0.3145524561405182, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:12:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:12:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:47] train_sft.py:596 - step 3179: {'train/loss': 0.012442268431186676, 'train/grad_norm': 0.3093265891075134, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:12:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:12:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:52] train_sft.py:596 - step 3180: {'train/loss': 0.014476433396339417, 'train/grad_norm': 0.6033408641815186, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 16:12:52] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:12:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:12:57] train_sft.py:596 - step 3181: {'train/loss': 0.013997822999954224, 'train/grad_norm': 0.5099058747291565, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:12:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:12:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:01] train_sft.py:596 - step 3182: {'train/loss': 0.014301412273198366, 'train/grad_norm': 0.43376120924949646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:13:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:13:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:06] train_sft.py:596 - step 3183: {'train/loss': 0.017246350646018982, 'train/grad_norm': 0.5910674333572388, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:13:06] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:13:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:11] train_sft.py:596 - step 3184: {'train/loss': 0.019892346113920212, 'train/grad_norm': 0.9783340096473694, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:13:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:13:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:16] train_sft.py:596 - step 3185: {'train/loss': 0.01911226287484169, 'train/grad_norm': 0.41027578711509705, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:13:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:13:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:20] train_sft.py:596 - step 3186: {'train/loss': 0.01005776971578598, 'train/grad_norm': 0.32092875242233276, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:13:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:13:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:25] train_sft.py:596 - step 3187: {'train/loss': 0.008440140634775162, 'train/grad_norm': 0.3786049783229828, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:13:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:13:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:30] train_sft.py:596 - step 3188: {'train/loss': 0.015648052096366882, 'train/grad_norm': 0.4771742820739746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:13:30] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:13:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:35] train_sft.py:596 - step 3189: {'train/loss': 0.014924516901373863, 'train/grad_norm': 0.3339320123195648, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:13:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:13:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:40] train_sft.py:596 - step 3190: {'train/loss': 0.01563924551010132, 'train/grad_norm': 0.45859766006469727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:13:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:13:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:44] train_sft.py:596 - step 3191: {'train/loss': 0.01258443365804851, 'train/grad_norm': 0.28492268919944763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 16:13:44] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:13:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:49] train_sft.py:596 - step 3192: {'train/loss': 0.02634783834218979, 'train/grad_norm': 0.5093513131141663, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 16:13:49] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:13:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:54] train_sft.py:596 - step 3193: {'train/loss': 0.01300746574997902, 'train/grad_norm': 0.4933575987815857, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:13:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:13:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:13:59] train_sft.py:596 - step 3194: {'train/loss': 0.013672232627868652, 'train/grad_norm': 0.30176421999931335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:13:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:13:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:04] train_sft.py:596 - step 3195: {'train/loss': 0.01381758600473404, 'train/grad_norm': 0.5150300860404968, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 16:14:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:14:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:09] train_sft.py:596 - step 3196: {'train/loss': 0.01002500206232071, 'train/grad_norm': 0.34721240401268005, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:14:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:14:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:14] train_sft.py:596 - step 3197: {'train/loss': 0.01593327522277832, 'train/grad_norm': 0.5300268530845642, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:14:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:14:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:19] train_sft.py:596 - step 3198: {'train/loss': 0.0099295973777771, 'train/grad_norm': 0.49486953020095825, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 16:14:19] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:14:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:24] train_sft.py:596 - step 3199: {'train/loss': 0.03859582543373108, 'train/grad_norm': 1.49033784866333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:14:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:14:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:29] train_sft.py:596 - step 3200: {'train/loss': 0.010519258677959442, 'train/grad_norm': 0.42842647433280945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:14:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:14:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:34] train_sft.py:596 - step 3201: {'train/loss': 0.011123888194561005, 'train/grad_norm': 0.5529128313064575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 16:14:34] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:14:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:38] train_sft.py:596 - step 3202: {'train/loss': 0.01759466528892517, 'train/grad_norm': 0.5267887115478516, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:14:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:14:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:43] train_sft.py:596 - step 3203: {'train/loss': 0.011482268571853638, 'train/grad_norm': 0.2817206084728241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:14:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:14:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:48] train_sft.py:596 - step 3204: {'train/loss': 0.014331534504890442, 'train/grad_norm': 0.48052871227264404, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:14:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:14:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:53] train_sft.py:596 - step 3205: {'train/loss': 0.019415274262428284, 'train/grad_norm': 0.5861402750015259, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:14:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:14:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:14:58] train_sft.py:596 - step 3206: {'train/loss': 0.018006786704063416, 'train/grad_norm': 0.49053463339805603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:14:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:14:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:03] train_sft.py:596 - step 3207: {'train/loss': 0.013482660055160522, 'train/grad_norm': 0.37734001874923706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 16:15:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:15:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:07] train_sft.py:596 - step 3208: {'train/loss': 0.01000472903251648, 'train/grad_norm': 0.43644842505455017, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:15:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:15:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:12] train_sft.py:596 - step 3209: {'train/loss': 0.012220829725265503, 'train/grad_norm': 0.3933956027030945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:15:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:15:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:17] train_sft.py:596 - step 3210: {'train/loss': 0.01364874467253685, 'train/grad_norm': 0.5466390252113342, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:15:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:15:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:21] train_sft.py:596 - step 3211: {'train/loss': 0.010948985815048218, 'train/grad_norm': 0.43123167753219604, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:15:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:15:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:26] train_sft.py:596 - step 3212: {'train/loss': 0.01868268847465515, 'train/grad_norm': 0.74476158618927, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:15:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:15:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:31] train_sft.py:596 - step 3213: {'train/loss': 0.012741193175315857, 'train/grad_norm': 0.28730422258377075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:15:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:15:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:35] train_sft.py:596 - step 3214: {'train/loss': 0.012512888759374619, 'train/grad_norm': 0.333012193441391, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:15:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:15:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:41] train_sft.py:596 - step 3215: {'train/loss': 0.01532021164894104, 'train/grad_norm': 0.4987794756889343, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:15:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:15:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:45] train_sft.py:596 - step 3216: {'train/loss': 0.01023295521736145, 'train/grad_norm': 0.4044322371482849, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:15:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:15:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:50] train_sft.py:596 - step 3217: {'train/loss': 0.013050928711891174, 'train/grad_norm': 0.6663914322853088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:15:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:15:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:15:54] train_sft.py:596 - step 3218: {'train/loss': 0.011529870331287384, 'train/grad_norm': 0.30673089623451233, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:15:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:15:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:00] train_sft.py:596 - step 3219: {'train/loss': 0.014818817377090454, 'train/grad_norm': 0.5563996434211731, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 16:16:00] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:16:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:05] train_sft.py:596 - step 3220: {'train/loss': 0.010043390095233917, 'train/grad_norm': 0.3704366385936737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:16:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:16:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:09] train_sft.py:596 - step 3221: {'train/loss': 0.015541918575763702, 'train/grad_norm': 0.37290051579475403, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:16:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:16:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:14] train_sft.py:596 - step 3222: {'train/loss': 0.010379955172538757, 'train/grad_norm': 0.38818517327308655, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:16:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:16:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:19] train_sft.py:596 - step 3223: {'train/loss': 0.013222485780715942, 'train/grad_norm': 0.2997294068336487, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 16:16:19] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:16:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:23] train_sft.py:596 - step 3224: {'train/loss': 0.02175050973892212, 'train/grad_norm': 0.5461986064910889, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:16:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:16:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:28] train_sft.py:596 - step 3225: {'train/loss': 0.024041742086410522, 'train/grad_norm': 0.8028437495231628, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:16:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:16:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:33] train_sft.py:596 - step 3226: {'train/loss': 0.008890017867088318, 'train/grad_norm': 0.6947896480560303, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:16:33] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:16:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:38] train_sft.py:596 - step 3227: {'train/loss': 0.014836445450782776, 'train/grad_norm': 0.3665149211883545, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:16:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:16:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:42] train_sft.py:596 - step 3228: {'train/loss': 0.014448322355747223, 'train/grad_norm': 0.5850001573562622, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:16:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:16:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:47] train_sft.py:596 - step 3229: {'train/loss': 0.01271548867225647, 'train/grad_norm': 0.8712975978851318, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:16:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:16:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:52] train_sft.py:596 - step 3230: {'train/loss': 0.013073667883872986, 'train/grad_norm': 0.5446794629096985, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 16:16:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:16:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:16:57] train_sft.py:596 - step 3231: {'train/loss': 0.015178516507148743, 'train/grad_norm': 0.40381258726119995, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:16:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:16:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:01] train_sft.py:596 - step 3232: {'train/loss': 0.014240771532058716, 'train/grad_norm': 0.3695179522037506, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:17:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:17:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:06] train_sft.py:596 - step 3233: {'train/loss': 0.011001377832144499, 'train/grad_norm': 0.4245215356349945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:17:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:17:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:11] train_sft.py:596 - step 3234: {'train/loss': 0.014979839324951172, 'train/grad_norm': 0.38391605019569397, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:17:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:17:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:16] train_sft.py:596 - step 3235: {'train/loss': 0.00895392894744873, 'train/grad_norm': 0.2886766791343689, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:17:16] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:17:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:20] train_sft.py:596 - step 3236: {'train/loss': 0.01870548725128174, 'train/grad_norm': 0.5101460218429565, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:17:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:17:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:25] train_sft.py:596 - step 3237: {'train/loss': 0.010241471230983734, 'train/grad_norm': 0.38783368468284607, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:17:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:17:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:29] train_sft.py:596 - step 3238: {'train/loss': 0.016382604837417603, 'train/grad_norm': 0.4962087571620941, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:17:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:17:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:34] train_sft.py:596 - step 3239: {'train/loss': 0.013477236032485962, 'train/grad_norm': 0.3427619934082031, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:17:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:17:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:39] train_sft.py:596 - step 3240: {'train/loss': 0.012850090861320496, 'train/grad_norm': 0.3513067662715912, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:17:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:17:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:44] train_sft.py:596 - step 3241: {'train/loss': 0.014547847211360931, 'train/grad_norm': 0.38971418142318726, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
[2025-12-29 16:17:44] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:17:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:49] train_sft.py:596 - step 3242: {'train/loss': 0.014209091663360596, 'train/grad_norm': 0.3466796576976776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:17:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:17:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:53] train_sft.py:596 - step 3243: {'train/loss': 0.009945675730705261, 'train/grad_norm': 0.3810214102268219, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:17:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:17:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:17:58] train_sft.py:596 - step 3244: {'train/loss': 0.01763135939836502, 'train/grad_norm': 0.39859941601753235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:17:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:17:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:03] train_sft.py:596 - step 3245: {'train/loss': 0.01904880057554692, 'train/grad_norm': 0.9460431337356567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:18:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:18:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:07] train_sft.py:596 - step 3246: {'train/loss': 0.015948619693517685, 'train/grad_norm': 0.48348087072372437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:18:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:18:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:12] train_sft.py:596 - step 3247: {'train/loss': 0.012803785502910614, 'train/grad_norm': 0.26809242367744446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:18:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:18:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:17] train_sft.py:596 - step 3248: {'train/loss': 0.010269112884998322, 'train/grad_norm': 0.20133966207504272, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:18:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:18:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:21] train_sft.py:596 - step 3249: {'train/loss': 0.017850399017333984, 'train/grad_norm': 0.7180132865905762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:18:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:18:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:26] train_sft.py:596 - step 3250: {'train/loss': 0.014271959662437439, 'train/grad_norm': 0.6871716380119324, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:18:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:18:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:31] train_sft.py:596 - step 3251: {'train/loss': 0.02139139175415039, 'train/grad_norm': 0.5959675908088684, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 16:18:31] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:18:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:35] train_sft.py:596 - step 3252: {'train/loss': 0.016591817140579224, 'train/grad_norm': 0.4453689754009247, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:18:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:18:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:40] train_sft.py:596 - step 3253: {'train/loss': 0.012045437470078468, 'train/grad_norm': 0.4984276294708252, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:18:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:18:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:45] train_sft.py:596 - step 3254: {'train/loss': 0.012541964650154114, 'train/grad_norm': 0.3384954631328583, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:18:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:18:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:50] train_sft.py:596 - step 3255: {'train/loss': 0.00967399775981903, 'train/grad_norm': 0.37004581093788147, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:18:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:18:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:54] train_sft.py:596 - step 3256: {'train/loss': 0.015194449806585908, 'train/grad_norm': 0.7133450508117676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:18:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:18:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:18:59] train_sft.py:596 - step 3257: {'train/loss': 0.010390736162662506, 'train/grad_norm': 0.3575023114681244, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:18:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:18:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:03] train_sft.py:596 - step 3258: {'train/loss': 0.009203914552927017, 'train/grad_norm': 0.31350719928741455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:19:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:19:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:08] train_sft.py:596 - step 3259: {'train/loss': 0.010771699249744415, 'train/grad_norm': 0.537455677986145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:19:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:19:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:13] train_sft.py:596 - step 3260: {'train/loss': 0.019001096487045288, 'train/grad_norm': 0.5714661478996277, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:19:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:19:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:18] train_sft.py:596 - step 3261: {'train/loss': 0.017072975635528564, 'train/grad_norm': 0.48787713050842285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 16:19:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:19:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:22] train_sft.py:596 - step 3262: {'train/loss': 0.020634546875953674, 'train/grad_norm': 0.43533098697662354, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:19:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:19:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:27] train_sft.py:596 - step 3263: {'train/loss': 0.022327139973640442, 'train/grad_norm': 0.4987044036388397, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:19:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:19:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:32] train_sft.py:596 - step 3264: {'train/loss': 0.014093488454818726, 'train/grad_norm': 0.4468255639076233, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:19:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:19:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:36] train_sft.py:596 - step 3265: {'train/loss': 0.01413320004940033, 'train/grad_norm': 0.518933117389679, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:19:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:19:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:41] train_sft.py:596 - step 3266: {'train/loss': 0.015294499695301056, 'train/grad_norm': 0.44487491250038147, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:19:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:19:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:46] train_sft.py:596 - step 3267: {'train/loss': 0.013171955943107605, 'train/grad_norm': 0.3240979313850403, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:19:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:19:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:51] train_sft.py:596 - step 3268: {'train/loss': 0.011815905570983887, 'train/grad_norm': 0.42331820726394653, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:19:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:19:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:19:55] train_sft.py:596 - step 3269: {'train/loss': 0.010988519992679358, 'train/grad_norm': 0.48392510414123535, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:19:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:19:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:00] train_sft.py:596 - step 3270: {'train/loss': 0.017292797565460205, 'train/grad_norm': 0.45804187655448914, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:20:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:05] train_sft.py:596 - step 3271: {'train/loss': 0.02966175600886345, 'train/grad_norm': 0.6135825514793396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:20:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:20:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:10] train_sft.py:596 - step 3272: {'train/loss': 0.010067731142044067, 'train/grad_norm': 0.2534506022930145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:20:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:20:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:14] train_sft.py:596 - step 3273: {'train/loss': 0.015217743813991547, 'train/grad_norm': 0.3425567150115967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:20:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:20:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:19] train_sft.py:596 - step 3274: {'train/loss': 0.00907742977142334, 'train/grad_norm': 0.18446511030197144, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:20:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:20:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:24] train_sft.py:596 - step 3275: {'train/loss': 0.013023912906646729, 'train/grad_norm': 0.42338308691978455, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:20:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:20:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:29] train_sft.py:596 - step 3276: {'train/loss': 0.012540817260742188, 'train/grad_norm': 0.39439716935157776, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:20:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:34] train_sft.py:596 - step 3277: {'train/loss': 0.02046029269695282, 'train/grad_norm': 0.7143304347991943, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:20:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:38] train_sft.py:596 - step 3278: {'train/loss': 0.009186536073684692, 'train/grad_norm': 0.19761072099208832, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:20:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:20:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:43] train_sft.py:596 - step 3279: {'train/loss': 0.018100246787071228, 'train/grad_norm': 0.6878717541694641, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:20:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:48] train_sft.py:596 - step 3280: {'train/loss': 0.016039114445447922, 'train/grad_norm': 0.6027486324310303, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:20:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:52] train_sft.py:596 - step 3281: {'train/loss': 0.011662788689136505, 'train/grad_norm': 0.28877484798431396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:20:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:20:57] train_sft.py:596 - step 3282: {'train/loss': 0.027209750842303038, 'train/grad_norm': 0.4227153956890106, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:20:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:20:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:02] train_sft.py:596 - step 3283: {'train/loss': 0.012237772345542908, 'train/grad_norm': 0.4370841979980469, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:21:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:21:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:07] train_sft.py:596 - step 3284: {'train/loss': 0.014544755220413208, 'train/grad_norm': 0.5191026926040649, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:21:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:21:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:11] train_sft.py:596 - step 3285: {'train/loss': 0.015660692006349564, 'train/grad_norm': 0.5235776901245117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:21:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:21:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:16] train_sft.py:596 - step 3286: {'train/loss': 0.016414934769272804, 'train/grad_norm': 0.3597243130207062, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:21:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:21:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:21] train_sft.py:596 - step 3287: {'train/loss': 0.011270537972450256, 'train/grad_norm': 0.3818446695804596, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 16:21:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:21:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:26] train_sft.py:596 - step 3288: {'train/loss': 0.01099223643541336, 'train/grad_norm': 0.4014202058315277, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:21:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:21:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:30] train_sft.py:596 - step 3289: {'train/loss': 0.012646280229091644, 'train/grad_norm': 0.5317866206169128, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:21:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:21:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:35] train_sft.py:596 - step 3290: {'train/loss': 0.017398685216903687, 'train/grad_norm': 0.4416581094264984, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:21:35] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:21:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:40] train_sft.py:596 - step 3291: {'train/loss': 0.011842817068099976, 'train/grad_norm': 0.32958921790122986, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 16:21:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:21:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:45] train_sft.py:596 - step 3292: {'train/loss': 0.013105295598506927, 'train/grad_norm': 0.3552810549736023, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:21:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:21:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:49] train_sft.py:596 - step 3293: {'train/loss': 0.015490841120481491, 'train/grad_norm': 0.35224318504333496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:21:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:21:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:54] train_sft.py:596 - step 3294: {'train/loss': 0.012279540300369263, 'train/grad_norm': 0.331924170255661, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:21:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:21:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:21:59] train_sft.py:596 - step 3295: {'train/loss': 0.012004494667053223, 'train/grad_norm': 0.33356159925460815, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:21:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:21:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:04] train_sft.py:596 - step 3296: {'train/loss': 0.017244946211576462, 'train/grad_norm': 0.36101558804512024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:22:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:22:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:08] train_sft.py:596 - step 3297: {'train/loss': 0.01215498149394989, 'train/grad_norm': 0.3437870144844055, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 16:22:08] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:22:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:13] train_sft.py:596 - step 3298: {'train/loss': 0.019481539726257324, 'train/grad_norm': 0.5396504998207092, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:22:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:22:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:18] train_sft.py:596 - step 3299: {'train/loss': 0.016639456152915955, 'train/grad_norm': 0.3125099241733551, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:22:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:22:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:22] train_sft.py:596 - step 3300: {'train/loss': 0.018369287252426147, 'train/grad_norm': 0.5367101430892944, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:22:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:22:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:27] train_sft.py:596 - step 3301: {'train/loss': 0.010443150997161865, 'train/grad_norm': 0.2526378035545349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:22:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:22:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:32] train_sft.py:596 - step 3302: {'train/loss': 0.017596542835235596, 'train/grad_norm': 0.36336585879325867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:22:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:22:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:37] train_sft.py:596 - step 3303: {'train/loss': 0.008503735065460205, 'train/grad_norm': 0.3455333411693573, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:22:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:22:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:42] train_sft.py:596 - step 3304: {'train/loss': 0.017813384532928467, 'train/grad_norm': 0.4194982051849365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:22:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:22:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:47] train_sft.py:596 - step 3305: {'train/loss': 0.017597150057554245, 'train/grad_norm': 0.35405436158180237, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:22:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:22:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:51] train_sft.py:596 - step 3306: {'train/loss': 0.009436123073101044, 'train/grad_norm': 0.2229217290878296, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:22:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:22:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:22:56] train_sft.py:596 - step 3307: {'train/loss': 0.02077968418598175, 'train/grad_norm': 0.5603241324424744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 16:22:56] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:22:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:01] train_sft.py:596 - step 3308: {'train/loss': 0.0168018639087677, 'train/grad_norm': 0.4360303282737732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:23:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:23:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:06] train_sft.py:596 - step 3309: {'train/loss': 0.013988442718982697, 'train/grad_norm': 0.5541728138923645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:23:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:23:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:10] train_sft.py:596 - step 3310: {'train/loss': 0.017873510718345642, 'train/grad_norm': 0.33609798550605774, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:23:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:23:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:15] train_sft.py:596 - step 3311: {'train/loss': 0.017361000180244446, 'train/grad_norm': 0.3793880045413971, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:23:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:23:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:20] train_sft.py:596 - step 3312: {'train/loss': 0.017177581787109375, 'train/grad_norm': 0.2865426242351532, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:23:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:23:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:24] train_sft.py:596 - step 3313: {'train/loss': 0.01296079158782959, 'train/grad_norm': 0.36628323793411255, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:23:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:23:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:29] train_sft.py:596 - step 3314: {'train/loss': 0.016974613070487976, 'train/grad_norm': 0.3897736370563507, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:23:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:23:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:34] train_sft.py:596 - step 3315: {'train/loss': 0.011441528797149658, 'train/grad_norm': 0.5848340392112732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:23:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:23:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:39] train_sft.py:596 - step 3316: {'train/loss': 0.00989881157875061, 'train/grad_norm': 0.2633785307407379, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:23:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:23:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:44] train_sft.py:596 - step 3317: {'train/loss': 0.01479356735944748, 'train/grad_norm': 0.4354560375213623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:23:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:23:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:48] train_sft.py:596 - step 3318: {'train/loss': 0.015163369476795197, 'train/grad_norm': 0.4015246331691742, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:23:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:23:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:53] train_sft.py:596 - step 3319: {'train/loss': 0.018533632159233093, 'train/grad_norm': 0.6896058320999146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:23:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:23:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:23:57] train_sft.py:596 - step 3320: {'train/loss': 0.017875881399959326, 'train/grad_norm': 0.5553615093231201, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:23:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:23:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:02] train_sft.py:596 - step 3321: {'train/loss': 0.015319541096687317, 'train/grad_norm': 0.3561573624610901, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:24:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:24:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:07] train_sft.py:596 - step 3322: {'train/loss': 0.028609678149223328, 'train/grad_norm': 0.29779866337776184, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:24:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:24:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:12] train_sft.py:596 - step 3323: {'train/loss': 0.013910174369812012, 'train/grad_norm': 0.33159536123275757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:24:12] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:24:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:16] train_sft.py:596 - step 3324: {'train/loss': 0.00952189415693283, 'train/grad_norm': 0.269176721572876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:24:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:24:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:21] train_sft.py:596 - step 3325: {'train/loss': 0.019105255603790283, 'train/grad_norm': 0.5645343661308289, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:24:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:24:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:26] train_sft.py:596 - step 3326: {'train/loss': 0.009965170174837112, 'train/grad_norm': 0.36390525102615356, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:24:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:24:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:31] train_sft.py:596 - step 3327: {'train/loss': 0.021873220801353455, 'train/grad_norm': 0.4259517192840576, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:24:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:24:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:36] train_sft.py:596 - step 3328: {'train/loss': 0.014328479766845703, 'train/grad_norm': 0.3321099579334259, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:24:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:24:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:40] train_sft.py:596 - step 3329: {'train/loss': 0.010647423565387726, 'train/grad_norm': 0.42707669734954834, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:24:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:24:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:45] train_sft.py:596 - step 3330: {'train/loss': 0.013226676732301712, 'train/grad_norm': 0.3464161455631256, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:24:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:24:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:49] train_sft.py:596 - step 3331: {'train/loss': 0.008180253207683563, 'train/grad_norm': 0.24053165316581726, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:24:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:24:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:54] train_sft.py:596 - step 3332: {'train/loss': 0.014942631125450134, 'train/grad_norm': 0.42675527930259705, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:24:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:24:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:24:59] train_sft.py:596 - step 3333: {'train/loss': 0.01938718929886818, 'train/grad_norm': 0.6218901872634888, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:24:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:24:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:04] train_sft.py:596 - step 3334: {'train/loss': 0.020795665681362152, 'train/grad_norm': 0.6185725927352905, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:25:04] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:25:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:08] train_sft.py:596 - step 3335: {'train/loss': 0.01314467191696167, 'train/grad_norm': 0.49610498547554016, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:25:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:25:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:13] train_sft.py:596 - step 3336: {'train/loss': 0.01278744637966156, 'train/grad_norm': 0.5138359069824219, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:25:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:25:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:18] train_sft.py:596 - step 3337: {'train/loss': 0.007839754223823547, 'train/grad_norm': 0.2757857143878937, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:25:18] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:25:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:23] train_sft.py:596 - step 3338: {'train/loss': 0.01631981134414673, 'train/grad_norm': 0.40563228726387024, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:25:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:25:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:27] train_sft.py:596 - step 3339: {'train/loss': 0.018376916646957397, 'train/grad_norm': 0.4385921061038971, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 16:25:27] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 16:25:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:32] train_sft.py:596 - step 3340: {'train/loss': 0.01400429755449295, 'train/grad_norm': 0.445661336183548, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:25:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:25:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:37] train_sft.py:596 - step 3341: {'train/loss': 0.021793246269226074, 'train/grad_norm': 0.7011025547981262, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:25:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:25:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:42] train_sft.py:596 - step 3342: {'train/loss': 0.01329319179058075, 'train/grad_norm': 0.7528876066207886, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.40s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.40s/it]
[2025-12-29 16:25:42] timer.py:32 - Timer actor_train end (elapsed: 5.4s)
[2025-12-29 16:25:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:47] train_sft.py:596 - step 3343: {'train/loss': 0.017736047506332397, 'train/grad_norm': 0.5238844752311707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:25:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:25:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:52] train_sft.py:596 - step 3344: {'train/loss': 0.013603687286376953, 'train/grad_norm': 0.36938467621803284, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 16:25:52] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:25:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:25:57] train_sft.py:596 - step 3345: {'train/loss': 0.01841259002685547, 'train/grad_norm': 0.5897203087806702, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:25:57] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:25:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:02] train_sft.py:596 - step 3346: {'train/loss': 0.013030990958213806, 'train/grad_norm': 0.3043402135372162, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:26:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:26:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:06] train_sft.py:596 - step 3347: {'train/loss': 0.013812318444252014, 'train/grad_norm': 0.359243780374527, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:26:06] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:26:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:11] train_sft.py:596 - step 3348: {'train/loss': 0.009698666632175446, 'train/grad_norm': 0.18462873995304108, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:26:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:26:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:16] train_sft.py:596 - step 3349: {'train/loss': 0.015330478549003601, 'train/grad_norm': 0.5325206518173218, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:26:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:26:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:20] train_sft.py:596 - step 3350: {'train/loss': 0.0173603817820549, 'train/grad_norm': 0.6097652912139893, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:26:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:26:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:25] train_sft.py:596 - step 3351: {'train/loss': 0.008791059255599976, 'train/grad_norm': 0.2404870241880417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:26:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:26:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:30] train_sft.py:596 - step 3352: {'train/loss': 0.01571469008922577, 'train/grad_norm': 0.45938774943351746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:26:30] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:26:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:35] train_sft.py:596 - step 3353: {'train/loss': 0.013416461646556854, 'train/grad_norm': 0.4178104102611542, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:26:35] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:26:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:40] train_sft.py:596 - step 3354: {'train/loss': 0.010858066380023956, 'train/grad_norm': 0.5475105047225952, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:26:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:26:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:44] train_sft.py:596 - step 3355: {'train/loss': 0.016678564250469208, 'train/grad_norm': 0.6250864267349243, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:26:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:26:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:49] train_sft.py:596 - step 3356: {'train/loss': 0.013360962271690369, 'train/grad_norm': 0.4480293095111847, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 16:26:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:26:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:54] train_sft.py:596 - step 3357: {'train/loss': 0.014614611864089966, 'train/grad_norm': 0.5304608345031738, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:26:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:26:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:26:59] train_sft.py:596 - step 3358: {'train/loss': 0.010281562805175781, 'train/grad_norm': 0.44081661105155945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:26:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:26:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:04] train_sft.py:596 - step 3359: {'train/loss': 0.019874274730682373, 'train/grad_norm': 0.4839535653591156, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:27:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:27:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:08] train_sft.py:596 - step 3360: {'train/loss': 0.01704275608062744, 'train/grad_norm': 0.38026919960975647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:27:08] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:27:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:13] train_sft.py:596 - step 3361: {'train/loss': 0.016784124076366425, 'train/grad_norm': 0.5489073991775513, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:27:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:27:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:18] train_sft.py:596 - step 3362: {'train/loss': 0.017238467931747437, 'train/grad_norm': 0.7116482853889465, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:27:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:27:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:22] train_sft.py:596 - step 3363: {'train/loss': 0.015345454216003418, 'train/grad_norm': 0.41827577352523804, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:27:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:27:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:27] train_sft.py:596 - step 3364: {'train/loss': 0.013438329100608826, 'train/grad_norm': 0.264452189207077, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 16:27:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:27:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:32] train_sft.py:596 - step 3365: {'train/loss': 0.018888995051383972, 'train/grad_norm': 0.7467280626296997, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:27:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:27:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:37] train_sft.py:596 - step 3366: {'train/loss': 0.011803962290287018, 'train/grad_norm': 0.4197732210159302, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:27:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:27:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:41] train_sft.py:596 - step 3367: {'train/loss': 0.013058558106422424, 'train/grad_norm': 0.30917418003082275, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:27:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:27:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:46] train_sft.py:596 - step 3368: {'train/loss': 0.008967895060777664, 'train/grad_norm': 0.24283739924430847, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:27:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:27:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:51] train_sft.py:596 - step 3369: {'train/loss': 0.020287901163101196, 'train/grad_norm': 0.621849536895752, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:27:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:27:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:27:55] train_sft.py:596 - step 3370: {'train/loss': 0.008880041539669037, 'train/grad_norm': 0.3643086552619934, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:27:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:27:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:00] train_sft.py:596 - step 3371: {'train/loss': 0.018495559692382812, 'train/grad_norm': 0.5856504440307617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:28:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:28:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:05] train_sft.py:596 - step 3372: {'train/loss': 0.021012336015701294, 'train/grad_norm': 0.5488290190696716, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:28:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:28:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:10] train_sft.py:596 - step 3373: {'train/loss': 0.011750929057598114, 'train/grad_norm': 0.4796520471572876, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 16:28:10] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:28:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:15] train_sft.py:596 - step 3374: {'train/loss': 0.01304682344198227, 'train/grad_norm': 0.30300194025039673, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 16:28:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:28:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:20] train_sft.py:596 - step 3375: {'train/loss': 0.014538809657096863, 'train/grad_norm': 0.39653682708740234, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:28:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:28:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:25] train_sft.py:596 - step 3376: {'train/loss': 0.013474494218826294, 'train/grad_norm': 0.28386929631233215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:28:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:28:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:30] train_sft.py:596 - step 3377: {'train/loss': 0.011987581849098206, 'train/grad_norm': 0.30741050839424133, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 16:28:30] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:28:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:35] train_sft.py:596 - step 3378: {'train/loss': 0.009909823536872864, 'train/grad_norm': 0.3468708097934723, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 16:28:35] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:28:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:39] train_sft.py:596 - step 3379: {'train/loss': 0.009807508438825607, 'train/grad_norm': 0.2057286947965622, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:28:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:28:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:44] train_sft.py:596 - step 3380: {'train/loss': 0.005563780665397644, 'train/grad_norm': 0.1721336841583252, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:28:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:28:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:49] train_sft.py:596 - step 3381: {'train/loss': 0.012786433100700378, 'train/grad_norm': 0.5262110829353333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:28:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:28:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:54] train_sft.py:596 - step 3382: {'train/loss': 0.010800592601299286, 'train/grad_norm': 0.3613346815109253, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:28:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:28:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:28:58] train_sft.py:596 - step 3383: {'train/loss': 0.006238933652639389, 'train/grad_norm': 0.16315500438213348, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:28:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:28:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:03] train_sft.py:596 - step 3384: {'train/loss': 0.007780753076076508, 'train/grad_norm': 0.16536200046539307, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:29:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:29:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:08] train_sft.py:596 - step 3385: {'train/loss': 0.015258103609085083, 'train/grad_norm': 0.4362245500087738, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:29:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:29:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:12] train_sft.py:596 - step 3386: {'train/loss': 0.008826736360788345, 'train/grad_norm': 0.3244974613189697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:29:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:29:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:17] train_sft.py:596 - step 3387: {'train/loss': 0.024508533999323845, 'train/grad_norm': 0.7837371230125427, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 16:29:17] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:29:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:23] train_sft.py:596 - step 3388: {'train/loss': 0.010404643602669239, 'train/grad_norm': 0.497331440448761, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 16:29:23] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:29:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:28] train_sft.py:596 - step 3389: {'train/loss': 0.010101322084665298, 'train/grad_norm': 0.4819449782371521, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 16:29:28] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:29:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:32] train_sft.py:596 - step 3390: {'train/loss': 0.009097801521420479, 'train/grad_norm': 0.34093961119651794, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:29:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:29:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:37] train_sft.py:596 - step 3391: {'train/loss': 0.010504957288503647, 'train/grad_norm': 0.6110138297080994, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:29:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:29:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:42] train_sft.py:596 - step 3392: {'train/loss': 0.011468715965747833, 'train/grad_norm': 0.4246058762073517, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:29:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:29:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:46] train_sft.py:596 - step 3393: {'train/loss': 0.016093231737613678, 'train/grad_norm': 0.5550819635391235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:29:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:29:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:51] train_sft.py:596 - step 3394: {'train/loss': 0.016816042363643646, 'train/grad_norm': 0.5444033145904541, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:29:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:29:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:29:56] train_sft.py:596 - step 3395: {'train/loss': 0.010401323437690735, 'train/grad_norm': 0.5228533744812012, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:29:56] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:29:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:00] train_sft.py:596 - step 3396: {'train/loss': 0.011200647801160812, 'train/grad_norm': 0.4388370215892792, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:30:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:30:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:05] train_sft.py:596 - step 3397: {'train/loss': 0.007026735693216324, 'train/grad_norm': 0.41727542877197266, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:30:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:30:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:10] train_sft.py:596 - step 3398: {'train/loss': 0.018523365259170532, 'train/grad_norm': 0.6078608632087708, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:30:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:30:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:15] train_sft.py:596 - step 3399: {'train/loss': 0.013114318251609802, 'train/grad_norm': 0.551499605178833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:30:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:30:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:19] train_sft.py:596 - step 3400: {'train/loss': 0.03244759887456894, 'train/grad_norm': 2.8047285079956055, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:30:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:30:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:25] train_sft.py:596 - step 3401: {'train/loss': 0.014435317367315292, 'train/grad_norm': 0.4204263985157013, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 16:30:25] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 16:30:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:29] train_sft.py:596 - step 3402: {'train/loss': 0.009674951434135437, 'train/grad_norm': 0.30550092458724976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:30:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:30:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:34] train_sft.py:596 - step 3403: {'train/loss': 0.011722102761268616, 'train/grad_norm': 0.17458806931972504, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 16:30:34] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:30:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:39] train_sft.py:596 - step 3404: {'train/loss': 0.004936084151268005, 'train/grad_norm': 0.17484533786773682, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 16:30:39] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:30:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:44] train_sft.py:596 - step 3405: {'train/loss': 0.0072638122364878654, 'train/grad_norm': 0.27381670475006104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 16:30:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:30:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:49] train_sft.py:596 - step 3406: {'train/loss': 0.013212189078330994, 'train/grad_norm': 0.3810630440711975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:30:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:30:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:54] train_sft.py:596 - step 3407: {'train/loss': 0.009139925241470337, 'train/grad_norm': 0.3229868710041046, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:30:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:30:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:30:59] train_sft.py:596 - step 3408: {'train/loss': 0.011685021221637726, 'train/grad_norm': 0.3080381751060486, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:30:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:30:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:03] train_sft.py:596 - step 3409: {'train/loss': 0.005780428647994995, 'train/grad_norm': 0.27192607522010803, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:31:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:31:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:08] train_sft.py:596 - step 3410: {'train/loss': 0.014223464066162705, 'train/grad_norm': 0.5827215313911438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:31:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:31:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:12] train_sft.py:596 - step 3411: {'train/loss': 0.0071855634450912476, 'train/grad_norm': 0.23740433156490326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:31:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:31:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:17] train_sft.py:596 - step 3412: {'train/loss': 0.016490399837493896, 'train/grad_norm': 0.3118375539779663, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:31:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:31:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:22] train_sft.py:596 - step 3413: {'train/loss': 0.013185769319534302, 'train/grad_norm': 0.6054887771606445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:31:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:31:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:26] train_sft.py:596 - step 3414: {'train/loss': 0.01270218938589096, 'train/grad_norm': 0.42952457070350647, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:31:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:31:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:31] train_sft.py:596 - step 3415: {'train/loss': 0.012823648750782013, 'train/grad_norm': 0.4263635277748108, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:31:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:31:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:36] train_sft.py:596 - step 3416: {'train/loss': 0.012304570525884628, 'train/grad_norm': 0.33504727482795715, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:31:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:31:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:41] train_sft.py:596 - step 3417: {'train/loss': 0.006206948310136795, 'train/grad_norm': 0.2757958769798279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 16:31:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:31:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:46] train_sft.py:596 - step 3418: {'train/loss': 0.009295947849750519, 'train/grad_norm': 0.3383867144584656, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:31:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:31:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:50] train_sft.py:596 - step 3419: {'train/loss': 0.020419780164957047, 'train/grad_norm': 0.44122326374053955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:31:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:31:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:31:55] train_sft.py:596 - step 3420: {'train/loss': 0.011640336364507675, 'train/grad_norm': 0.45818057656288147, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:31:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:31:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:00] train_sft.py:596 - step 3421: {'train/loss': 0.01664929836988449, 'train/grad_norm': 0.5220983624458313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 16:32:00] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:32:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:05] train_sft.py:596 - step 3422: {'train/loss': 0.016053590923547745, 'train/grad_norm': 0.5215939283370972, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:32:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:32:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:09] train_sft.py:596 - step 3423: {'train/loss': 0.007508963346481323, 'train/grad_norm': 0.21157358586788177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:32:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:32:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:14] train_sft.py:596 - step 3424: {'train/loss': 0.009298053104430437, 'train/grad_norm': 0.4018882215023041, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 16:32:14] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:32:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:19] train_sft.py:596 - step 3425: {'train/loss': 0.013652503490447998, 'train/grad_norm': 0.4731806814670563, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 16:32:19] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:32:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:24] train_sft.py:596 - step 3426: {'train/loss': 0.013453066349029541, 'train/grad_norm': 0.2748589515686035, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:32:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:32:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:29] train_sft.py:596 - step 3427: {'train/loss': 0.016560982912778854, 'train/grad_norm': 0.44290152192115784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:32:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:32:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:33] train_sft.py:596 - step 3428: {'train/loss': 0.012023495975881815, 'train/grad_norm': 0.37753257155418396, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:32:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:32:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:39] train_sft.py:596 - step 3429: {'train/loss': 0.009241566061973572, 'train/grad_norm': 0.5589216351509094, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.25s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.25s/it]
[2025-12-29 16:32:39] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 16:32:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:43] train_sft.py:596 - step 3430: {'train/loss': 0.01303112879395485, 'train/grad_norm': 0.2861582338809967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:32:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:32:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:48] train_sft.py:596 - step 3431: {'train/loss': 0.010747909545898438, 'train/grad_norm': 0.3462548553943634, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:32:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:32:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:52] train_sft.py:596 - step 3432: {'train/loss': 0.01578877866268158, 'train/grad_norm': 0.4063425660133362, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:32:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:32:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:32:57] train_sft.py:596 - step 3433: {'train/loss': 0.009410956874489784, 'train/grad_norm': 0.2735007107257843, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 16:32:57] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:32:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:02] train_sft.py:596 - step 3434: {'train/loss': 0.007595375180244446, 'train/grad_norm': 0.22738589346408844, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:33:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:33:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:06] train_sft.py:596 - step 3435: {'train/loss': 0.02769748866558075, 'train/grad_norm': 0.5216073989868164, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:33:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:33:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:11] train_sft.py:596 - step 3436: {'train/loss': 0.013695470988750458, 'train/grad_norm': 0.7389170527458191, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:33:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:33:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:16] train_sft.py:596 - step 3437: {'train/loss': 0.017559722065925598, 'train/grad_norm': 0.46971315145492554, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:33:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:33:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:20] train_sft.py:596 - step 3438: {'train/loss': 0.007549099624156952, 'train/grad_norm': 0.18334949016571045, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:33:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:33:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:25] train_sft.py:596 - step 3439: {'train/loss': 0.021139636635780334, 'train/grad_norm': 0.5357006788253784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:33:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:33:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:30] train_sft.py:596 - step 3440: {'train/loss': 0.012827347964048386, 'train/grad_norm': 0.549217700958252, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:33:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:33:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:34] train_sft.py:596 - step 3441: {'train/loss': 0.02014635130763054, 'train/grad_norm': 1.3250840902328491, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:33:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:33:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:39] train_sft.py:596 - step 3442: {'train/loss': 0.011684156954288483, 'train/grad_norm': 0.3922509551048279, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:33:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:33:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:44] train_sft.py:596 - step 3443: {'train/loss': 0.007085466757416725, 'train/grad_norm': 0.2660885155200958, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:33:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:33:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:48] train_sft.py:596 - step 3444: {'train/loss': 0.011678963899612427, 'train/grad_norm': 0.41054970026016235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:33:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:33:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:53] train_sft.py:596 - step 3445: {'train/loss': 0.009821107611060143, 'train/grad_norm': 0.37488868832588196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 16:33:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:33:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:33:58] train_sft.py:596 - step 3446: {'train/loss': 0.015758588910102844, 'train/grad_norm': 0.339103102684021, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:33:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:33:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:03] train_sft.py:596 - step 3447: {'train/loss': 0.011721968650817871, 'train/grad_norm': 0.251355916261673, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:34:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:34:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:07] train_sft.py:596 - step 3448: {'train/loss': 0.007755853235721588, 'train/grad_norm': 0.18890772759914398, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:34:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:34:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:12] train_sft.py:596 - step 3449: {'train/loss': 0.018404625356197357, 'train/grad_norm': 0.7390261888504028, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:34:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:34:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:16] train_sft.py:596 - step 3450: {'train/loss': 0.010284915566444397, 'train/grad_norm': 0.30292370915412903, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:34:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:34:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:21] train_sft.py:596 - step 3451: {'train/loss': 0.009498178958892822, 'train/grad_norm': 0.3480353355407715, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:34:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:34:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:26] train_sft.py:596 - step 3452: {'train/loss': 0.017115414142608643, 'train/grad_norm': 0.4252051115036011, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:34:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:34:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:31] train_sft.py:596 - step 3453: {'train/loss': 0.015412896871566772, 'train/grad_norm': 0.48182252049446106, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:34:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:34:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:35] train_sft.py:596 - step 3454: {'train/loss': 0.018298044800758362, 'train/grad_norm': 0.40262091159820557, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:34:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:34:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:41] train_sft.py:596 - step 3455: {'train/loss': 0.013738036155700684, 'train/grad_norm': 0.4214105010032654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 16:34:41] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:34:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:45] train_sft.py:596 - step 3456: {'train/loss': 0.019038349390029907, 'train/grad_norm': 0.7043543457984924, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:34:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:34:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:50] train_sft.py:596 - step 3457: {'train/loss': 0.01812686026096344, 'train/grad_norm': 0.477678120136261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:34:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:34:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:34:55] train_sft.py:596 - step 3458: {'train/loss': 0.015914298593997955, 'train/grad_norm': 0.4835537374019623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:34:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:34:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:00] train_sft.py:596 - step 3459: {'train/loss': 0.015348374843597412, 'train/grad_norm': 0.5161768794059753, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:35:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:35:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:04] train_sft.py:596 - step 3460: {'train/loss': 0.015217840671539307, 'train/grad_norm': 0.4778091013431549, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 16:35:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:35:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:09] train_sft.py:596 - step 3461: {'train/loss': 0.01542346179485321, 'train/grad_norm': 0.4126873314380646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:35:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:35:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:14] train_sft.py:596 - step 3462: {'train/loss': 0.01603558659553528, 'train/grad_norm': 0.6107086539268494, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:35:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:35:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:19] train_sft.py:596 - step 3463: {'train/loss': 0.008712738752365112, 'train/grad_norm': 0.38691702485084534, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:35:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:35:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:24] train_sft.py:596 - step 3464: {'train/loss': 0.012834839522838593, 'train/grad_norm': 0.42592573165893555, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:35:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:35:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:29] train_sft.py:596 - step 3465: {'train/loss': 0.019996225833892822, 'train/grad_norm': 0.6315706372261047, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:35:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:35:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:33] train_sft.py:596 - step 3466: {'train/loss': 0.016100235283374786, 'train/grad_norm': 0.5291495323181152, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:35:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:35:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:38] train_sft.py:596 - step 3467: {'train/loss': 0.010636589489877224, 'train/grad_norm': 0.30116647481918335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:35:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:35:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:42] train_sft.py:596 - step 3468: {'train/loss': 0.010587777942419052, 'train/grad_norm': 0.47389134764671326, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:35:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:35:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:47] train_sft.py:596 - step 3469: {'train/loss': 0.01069910079240799, 'train/grad_norm': 0.24728426337242126, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:35:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:35:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:52] train_sft.py:596 - step 3470: {'train/loss': 0.011454641819000244, 'train/grad_norm': 0.33351022005081177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:35:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:35:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:35:57] train_sft.py:596 - step 3471: {'train/loss': 0.015952512621879578, 'train/grad_norm': 0.3870881497859955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:35:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:35:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:01] train_sft.py:596 - step 3472: {'train/loss': 0.007461585104465485, 'train/grad_norm': 0.21967053413391113, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:36:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:36:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:06] train_sft.py:596 - step 3473: {'train/loss': 0.010618910193443298, 'train/grad_norm': 0.251450777053833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:36:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:36:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:11] train_sft.py:596 - step 3474: {'train/loss': 0.012978479266166687, 'train/grad_norm': 0.2751322090625763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:36:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:36:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:16] train_sft.py:596 - step 3475: {'train/loss': 0.015023885294795036, 'train/grad_norm': 0.6372029185295105, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:36:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:36:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:21] train_sft.py:596 - step 3476: {'train/loss': 0.012195706367492676, 'train/grad_norm': 0.5384187698364258, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:36:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:36:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:25] train_sft.py:596 - step 3477: {'train/loss': 0.01569487154483795, 'train/grad_norm': 0.3644524812698364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:36:25] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:36:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:30] train_sft.py:596 - step 3478: {'train/loss': 0.013216160237789154, 'train/grad_norm': 0.5075991153717041, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:36:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:36:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:35] train_sft.py:596 - step 3479: {'train/loss': 0.017284289002418518, 'train/grad_norm': 0.39578935503959656, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:36:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:36:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:40] train_sft.py:596 - step 3480: {'train/loss': 0.016271598637104034, 'train/grad_norm': 0.5906282067298889, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:36:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:36:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:44] train_sft.py:596 - step 3481: {'train/loss': 0.015781313180923462, 'train/grad_norm': 0.6003719568252563, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:36:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:36:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:49] train_sft.py:596 - step 3482: {'train/loss': 0.014442208223044872, 'train/grad_norm': 0.43382853269577026, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:36:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:36:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:54] train_sft.py:596 - step 3483: {'train/loss': 0.01361769437789917, 'train/grad_norm': 0.5669254660606384, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:36:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:36:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:36:58] train_sft.py:596 - step 3484: {'train/loss': 0.018311187624931335, 'train/grad_norm': 0.5733926892280579, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:36:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:36:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:03] train_sft.py:596 - step 3485: {'train/loss': 0.007807813584804535, 'train/grad_norm': 0.2322482317686081, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:37:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:37:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:08] train_sft.py:596 - step 3486: {'train/loss': 0.011799067258834839, 'train/grad_norm': 0.3961874544620514, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:37:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:37:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:12] train_sft.py:596 - step 3487: {'train/loss': 0.012768302112817764, 'train/grad_norm': 0.5565088987350464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:37:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:37:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:17] train_sft.py:596 - step 3488: {'train/loss': 0.014094889163970947, 'train/grad_norm': 0.28749510645866394, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:37:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:37:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:22] train_sft.py:596 - step 3489: {'train/loss': 0.014248430728912354, 'train/grad_norm': 0.40260523557662964, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:37:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:37:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:27] train_sft.py:596 - step 3490: {'train/loss': 0.0184844508767128, 'train/grad_norm': 0.37576115131378174, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:37:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:37:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:32] train_sft.py:596 - step 3491: {'train/loss': 0.011751584708690643, 'train/grad_norm': 0.3701784312725067, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 16:37:32] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:37:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:36] train_sft.py:596 - step 3492: {'train/loss': 0.010324221104383469, 'train/grad_norm': 0.3654360771179199, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:37:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:37:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:41] train_sft.py:596 - step 3493: {'train/loss': 0.01598767191171646, 'train/grad_norm': 0.46687814593315125, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:37:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:37:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:46] train_sft.py:596 - step 3494: {'train/loss': 0.010325536131858826, 'train/grad_norm': 0.2909800410270691, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:37:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:37:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:51] train_sft.py:596 - step 3495: {'train/loss': 0.006279405206441879, 'train/grad_norm': 0.20337459444999695, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 16:37:51] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:37:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:37:56] train_sft.py:596 - step 3496: {'train/loss': 0.011218585073947906, 'train/grad_norm': 0.3387272357940674, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:37:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:37:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:38:00] train_sft.py:596 - step 3497: {'train/loss': 0.02334083616733551, 'train/grad_norm': 0.4217875599861145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:38:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:38:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:38:05] train_sft.py:596 - step 3498: {'train/loss': 0.017877385020256042, 'train/grad_norm': 0.940651535987854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:38:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:38:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:38:10] train_sft.py:596 - step 3499: {'train/loss': 0.01520879752933979, 'train/grad_norm': 0.7451329231262207, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:38:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:01<02:48,  1.70s/it]
actor_val:   2%|▏         | 2/100 [00:03<02:53,  1.77s/it]
actor_val:   3%|▎         | 3/100 [00:05<02:42,  1.67s/it]
actor_val:   4%|▍         | 4/100 [00:06<02:35,  1.62s/it]
actor_val:   5%|▌         | 5/100 [00:08<02:30,  1.58s/it]
actor_val:   6%|▌         | 6/100 [00:09<02:26,  1.56s/it]
actor_val:   7%|▋         | 7/100 [00:11<02:24,  1.55s/it]
actor_val:   8%|▊         | 8/100 [00:12<02:22,  1.55s/it]
actor_val:   9%|▉         | 9/100 [00:14<02:29,  1.64s/it]
actor_val:  10%|█         | 10/100 [00:16<02:25,  1.61s/it]
actor_val:  11%|█         | 11/100 [00:17<02:21,  1.59s/it]
actor_val:  12%|█▏        | 12/100 [00:19<02:18,  1.58s/it]
actor_val:  13%|█▎        | 13/100 [00:20<02:17,  1.58s/it]
actor_val:  14%|█▍        | 14/100 [00:22<02:15,  1.58s/it]
actor_val:  15%|█▌        | 15/100 [00:24<02:20,  1.65s/it]
actor_val:  16%|█▌        | 16/100 [00:25<02:15,  1.61s/it]
actor_val:  17%|█▋        | 17/100 [00:27<02:20,  1.69s/it]
actor_val:  18%|█▊        | 18/100 [00:29<02:15,  1.65s/it]
actor_val:  19%|█▉        | 19/100 [00:30<02:11,  1.62s/it]
actor_val:  20%|██        | 20/100 [00:32<02:08,  1.61s/it]
actor_val:  21%|██        | 21/100 [00:34<02:10,  1.65s/it]
actor_val:  22%|██▏       | 22/100 [00:35<02:11,  1.69s/it]
actor_val:  23%|██▎       | 23/100 [00:37<02:06,  1.64s/it]
actor_val:  24%|██▍       | 24/100 [00:39<02:08,  1.70s/it]
actor_val:  25%|██▌       | 25/100 [00:40<02:02,  1.64s/it]
actor_val:  26%|██▌       | 26/100 [00:42<02:04,  1.68s/it]
actor_val:  27%|██▋       | 27/100 [00:44<02:04,  1.71s/it]
actor_val:  28%|██▊       | 28/100 [00:46<02:06,  1.76s/it]
actor_val:  29%|██▉       | 29/100 [00:47<02:00,  1.69s/it]
actor_val:  30%|███       | 30/100 [00:49<01:55,  1.65s/it]
actor_val:  31%|███       | 31/100 [00:50<01:51,  1.62s/it]
actor_val:  32%|███▏      | 32/100 [00:52<01:54,  1.68s/it]
actor_val:  33%|███▎      | 33/100 [00:54<01:49,  1.64s/it]
actor_val:  34%|███▍      | 34/100 [00:55<01:46,  1.61s/it]
actor_val:  35%|███▌      | 35/100 [00:57<01:42,  1.58s/it]
actor_val:  36%|███▌      | 36/100 [00:58<01:40,  1.56s/it]
actor_val:  37%|███▋      | 37/100 [01:00<01:43,  1.64s/it]
actor_val:  38%|███▊      | 38/100 [01:02<01:39,  1.61s/it]
actor_val:  39%|███▉      | 39/100 [01:03<01:37,  1.60s/it]
actor_val:  40%|████      | 40/100 [01:05<01:34,  1.58s/it]
actor_val:  41%|████      | 41/100 [01:06<01:32,  1.57s/it]
actor_val:  42%|████▏     | 42/100 [01:08<01:35,  1.65s/it]
actor_val:  43%|████▎     | 43/100 [01:10<01:32,  1.62s/it]
actor_val:  44%|████▍     | 44/100 [01:11<01:33,  1.66s/it]
actor_val:  45%|████▌     | 45/100 [01:13<01:29,  1.64s/it]
actor_val:  46%|████▌     | 46/100 [01:15<01:30,  1.67s/it]
actor_val:  47%|████▋     | 47/100 [01:16<01:30,  1.71s/it]
actor_val:  48%|████▊     | 48/100 [01:18<01:26,  1.66s/it]
actor_val:  49%|████▉     | 49/100 [01:20<01:23,  1.63s/it]
actor_val:  50%|█████     | 50/100 [01:21<01:20,  1.60s/it]
actor_val:  51%|█████     | 51/100 [01:23<01:17,  1.58s/it]
actor_val:  52%|█████▏    | 52/100 [01:24<01:19,  1.65s/it]
actor_val:  53%|█████▎    | 53/100 [01:26<01:15,  1.61s/it]
actor_val:  54%|█████▍    | 54/100 [01:28<01:17,  1.69s/it]
actor_val:  55%|█████▌    | 55/100 [01:30<01:17,  1.72s/it]
actor_val:  56%|█████▌    | 56/100 [01:31<01:13,  1.67s/it]
actor_val:  57%|█████▋    | 57/100 [01:33<01:10,  1.63s/it]
actor_val:  58%|█████▊    | 58/100 [01:34<01:07,  1.62s/it]
actor_val:  59%|█████▉    | 59/100 [01:36<01:05,  1.59s/it]
actor_val:  60%|██████    | 60/100 [01:38<01:05,  1.64s/it]
actor_val:  61%|██████    | 61/100 [01:39<01:05,  1.68s/it]
actor_val:  62%|██████▏   | 62/100 [01:41<01:02,  1.65s/it]
actor_val:  63%|██████▎   | 63/100 [01:42<01:00,  1.62s/it]
actor_val:  64%|██████▍   | 64/100 [01:44<01:00,  1.67s/it]
actor_val:  65%|██████▌   | 65/100 [01:46<00:57,  1.63s/it]
actor_val:  66%|██████▌   | 66/100 [01:47<00:55,  1.62s/it]
actor_val:  67%|██████▋   | 67/100 [01:49<00:52,  1.59s/it]
actor_val:  68%|██████▊   | 68/100 [01:50<00:50,  1.58s/it]
actor_val:  69%|██████▉   | 69/100 [01:52<00:48,  1.56s/it]
actor_val:  70%|███████   | 70/100 [01:54<00:47,  1.57s/it]
actor_val:  71%|███████   | 71/100 [01:55<00:45,  1.57s/it]
actor_val:  72%|███████▏  | 72/100 [01:57<00:43,  1.56s/it]
actor_val:  73%|███████▎  | 73/100 [01:58<00:41,  1.55s/it]
actor_val:  74%|███████▍  | 74/100 [02:00<00:40,  1.55s/it]
actor_val:  75%|███████▌  | 75/100 [02:01<00:38,  1.54s/it]
actor_val:  76%|███████▌  | 76/100 [02:03<00:38,  1.61s/it]
actor_val:  77%|███████▋  | 77/100 [02:05<00:36,  1.59s/it]
actor_val:  78%|███████▊  | 78/100 [02:06<00:36,  1.64s/it]
actor_val:  79%|███████▉  | 79/100 [02:08<00:33,  1.61s/it]
actor_val:  80%|████████  | 80/100 [02:10<00:33,  1.66s/it]
actor_val:  81%|████████  | 81/100 [02:11<00:32,  1.70s/it]
actor_val:  82%|████████▏ | 82/100 [02:13<00:31,  1.73s/it]
actor_val:  83%|████████▎ | 83/100 [02:15<00:28,  1.68s/it]
actor_val:  84%|████████▍ | 84/100 [02:16<00:26,  1.65s/it]
actor_val:  85%|████████▌ | 85/100 [02:18<00:24,  1.61s/it]
actor_val:  86%|████████▌ | 86/100 [02:20<00:23,  1.67s/it]
actor_val:  87%|████████▋ | 87/100 [02:22<00:22,  1.70s/it]
actor_val:  88%|████████▊ | 88/100 [02:23<00:19,  1.66s/it]
actor_val:  89%|████████▉ | 89/100 [02:25<00:18,  1.71s/it]
actor_val:  90%|█████████ | 90/100 [02:26<00:16,  1.65s/it]
actor_val:  91%|█████████ | 91/100 [02:28<00:14,  1.62s/it]
actor_val:  92%|█████████▏| 92/100 [02:29<00:12,  1.59s/it]
actor_val:  93%|█████████▎| 93/100 [02:31<00:10,  1.57s/it]
actor_val:  94%|█████████▍| 94/100 [02:33<00:09,  1.56s/it]
actor_val:  95%|█████████▌| 95/100 [02:34<00:07,  1.56s/it]
actor_val:  96%|█████████▌| 96/100 [02:36<00:06,  1.56s/it]
actor_val:  97%|█████████▋| 97/100 [02:37<00:04,  1.63s/it]
actor_val:  98%|█████████▊| 98/100 [02:39<00:03,  1.60s/it]
actor_val:  99%|█████████▉| 99/100 [02:41<00:01,  1.66s/it]
actor_val: 100%|██████████| 100/100 [02:43<00:00,  1.72s/it]
actor_val: 100%|██████████| 100/100 [02:43<00:00,  1.63s/it]
[2025-12-29 16:40:53] train_sft.py:658 - step 3500: {'val/loss': 0.46828125}
[2025-12-29 16:40:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:40:58] train_sft.py:596 - step 3500: {'train/loss': 0.011282555758953094, 'train/grad_norm': 0.28022122383117676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 16:40:58] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:40:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:03] train_sft.py:596 - step 3501: {'train/loss': 0.013899341225624084, 'train/grad_norm': 0.4717511236667633, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 16:41:03] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:41:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:08] train_sft.py:596 - step 3502: {'train/loss': 0.01711498200893402, 'train/grad_norm': 0.57463538646698, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:41:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:41:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:12] train_sft.py:596 - step 3503: {'train/loss': 0.0076621174812316895, 'train/grad_norm': 0.2614274024963379, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:41:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:41:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:17] train_sft.py:596 - step 3504: {'train/loss': 0.007711052894592285, 'train/grad_norm': 0.403856486082077, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:41:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:41:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:22] train_sft.py:596 - step 3505: {'train/loss': 0.013591349124908447, 'train/grad_norm': 0.34854698181152344, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:41:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:41:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:26] train_sft.py:596 - step 3506: {'train/loss': 0.014450982213020325, 'train/grad_norm': 0.35533565282821655, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:41:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:41:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:31] train_sft.py:596 - step 3507: {'train/loss': 0.01557963341474533, 'train/grad_norm': 0.2978367507457733, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:41:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:41:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:36] train_sft.py:596 - step 3508: {'train/loss': 0.012362994253635406, 'train/grad_norm': 0.36303985118865967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:41:36] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:41:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:41] train_sft.py:596 - step 3509: {'train/loss': 0.017354696989059448, 'train/grad_norm': 0.5908364057540894, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 16:41:41] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:41:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:46] train_sft.py:596 - step 3510: {'train/loss': 0.014756068587303162, 'train/grad_norm': 0.2464936226606369, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:41:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:41:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:50] train_sft.py:596 - step 3511: {'train/loss': 0.007010653614997864, 'train/grad_norm': 0.28692612051963806, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:41:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:41:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:41:55] train_sft.py:596 - step 3512: {'train/loss': 0.011784911155700684, 'train/grad_norm': 0.3349463641643524, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:41:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:41:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:00] train_sft.py:596 - step 3513: {'train/loss': 0.015162557363510132, 'train/grad_norm': 0.5121734738349915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:42:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:42:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:05] train_sft.py:596 - step 3514: {'train/loss': 0.012511122971773148, 'train/grad_norm': 0.40212735533714294, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:42:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:42:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:10] train_sft.py:596 - step 3515: {'train/loss': 0.018520884215831757, 'train/grad_norm': 0.3714849650859833, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 16:42:10] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:42:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:15] train_sft.py:596 - step 3516: {'train/loss': 0.011840488761663437, 'train/grad_norm': 0.27299764752388, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:42:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:42:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:19] train_sft.py:596 - step 3517: {'train/loss': 0.011006224900484085, 'train/grad_norm': 0.34673354029655457, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:42:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:42:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:24] train_sft.py:596 - step 3518: {'train/loss': 0.012132667005062103, 'train/grad_norm': 0.3548947274684906, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:42:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:42:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:29] train_sft.py:596 - step 3519: {'train/loss': 0.021086446940898895, 'train/grad_norm': 0.5770489573478699, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:42:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:42:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:34] train_sft.py:596 - step 3520: {'train/loss': 0.021038860082626343, 'train/grad_norm': 0.6034901738166809, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 16:42:34] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:42:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:39] train_sft.py:596 - step 3521: {'train/loss': 0.00885540246963501, 'train/grad_norm': 0.39334550499916077, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:42:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:42:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:43] train_sft.py:596 - step 3522: {'train/loss': 0.011385336983948946, 'train/grad_norm': 0.37802571058273315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:42:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:42:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:48] train_sft.py:596 - step 3523: {'train/loss': 0.017445320263504982, 'train/grad_norm': 0.34468987584114075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:42:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:42:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:53] train_sft.py:596 - step 3524: {'train/loss': 0.01317518949508667, 'train/grad_norm': 0.33592167496681213, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:42:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:42:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:42:58] train_sft.py:596 - step 3525: {'train/loss': 0.013087004888802767, 'train/grad_norm': 0.27054643630981445, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:42:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:42:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:02] train_sft.py:596 - step 3526: {'train/loss': 0.015890464186668396, 'train/grad_norm': 0.4955465793609619, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:43:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:43:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:08] train_sft.py:596 - step 3527: {'train/loss': 0.015125419944524765, 'train/grad_norm': 0.5432833433151245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 16:43:08] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:43:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:12] train_sft.py:596 - step 3528: {'train/loss': 0.012832213193178177, 'train/grad_norm': 0.4229128658771515, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:43:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:43:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:17] train_sft.py:596 - step 3529: {'train/loss': 0.009267696761526167, 'train/grad_norm': 0.19437134265899658, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:43:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:43:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:22] train_sft.py:596 - step 3530: {'train/loss': 0.01740311086177826, 'train/grad_norm': 0.5505887866020203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:43:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:43:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:27] train_sft.py:596 - step 3531: {'train/loss': 0.011248089373111725, 'train/grad_norm': 0.46282583475112915, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:43:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:43:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:32] train_sft.py:596 - step 3532: {'train/loss': 0.013898905366659164, 'train/grad_norm': 0.3951660692691803, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:43:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:43:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:37] train_sft.py:596 - step 3533: {'train/loss': 0.014439918100833893, 'train/grad_norm': 0.3794388175010681, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.13s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.14s/it]
[2025-12-29 16:43:37] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 16:43:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:42] train_sft.py:596 - step 3534: {'train/loss': 0.013354353606700897, 'train/grad_norm': 0.29611483216285706, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:43:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:43:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:46] train_sft.py:596 - step 3535: {'train/loss': 0.017461776733398438, 'train/grad_norm': 0.5085300803184509, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:43:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:43:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:51] train_sft.py:596 - step 3536: {'train/loss': 0.015033658593893051, 'train/grad_norm': 0.338339239358902, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:43:51] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:43:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:43:56] train_sft.py:596 - step 3537: {'train/loss': 0.008829474449157715, 'train/grad_norm': 0.2439011186361313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:43:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:43:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:01] train_sft.py:596 - step 3538: {'train/loss': 0.016791850328445435, 'train/grad_norm': 0.7829402089118958, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:44:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:44:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:06] train_sft.py:596 - step 3539: {'train/loss': 0.01175505667924881, 'train/grad_norm': 0.4149143397808075, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 16:44:06] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:44:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:10] train_sft.py:596 - step 3540: {'train/loss': 0.009876394644379616, 'train/grad_norm': 0.5207149982452393, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:44:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:44:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:15] train_sft.py:596 - step 3541: {'train/loss': 0.015393123030662537, 'train/grad_norm': 0.4278634488582611, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:44:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:44:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:19] train_sft.py:596 - step 3542: {'train/loss': 0.014912329614162445, 'train/grad_norm': 0.3454766869544983, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:44:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:44:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:24] train_sft.py:596 - step 3543: {'train/loss': 0.017277121543884277, 'train/grad_norm': 0.5759958028793335, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:44:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:44:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:29] train_sft.py:596 - step 3544: {'train/loss': 0.014385074377059937, 'train/grad_norm': 0.3454594016075134, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:44:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:44:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:34] train_sft.py:596 - step 3545: {'train/loss': 0.00892573595046997, 'train/grad_norm': 0.30433642864227295, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:44:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:44:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:39] train_sft.py:596 - step 3546: {'train/loss': 0.01742839813232422, 'train/grad_norm': 0.2864435911178589, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:44:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:44:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:43] train_sft.py:596 - step 3547: {'train/loss': 0.022904515266418457, 'train/grad_norm': 0.5543049573898315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:44:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:44:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:48] train_sft.py:596 - step 3548: {'train/loss': 0.013977661728858948, 'train/grad_norm': 0.23798377811908722, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:44:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:44:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:53] train_sft.py:596 - step 3549: {'train/loss': 0.014974357560276985, 'train/grad_norm': 0.4326417148113251, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:44:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:44:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:44:57] train_sft.py:596 - step 3550: {'train/loss': 0.015107374638319016, 'train/grad_norm': 0.3446519076824188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:44:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:44:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:02] train_sft.py:596 - step 3551: {'train/loss': 0.024058282375335693, 'train/grad_norm': 0.4320691227912903, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:45:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:07] train_sft.py:596 - step 3552: {'train/loss': 0.014082014560699463, 'train/grad_norm': 0.5746744871139526, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:45:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:45:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:12] train_sft.py:596 - step 3553: {'train/loss': 0.013040035963058472, 'train/grad_norm': 0.3775988817214966, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:45:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:16] train_sft.py:596 - step 3554: {'train/loss': 0.02285844087600708, 'train/grad_norm': 0.5728958249092102, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:45:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:45:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:21] train_sft.py:596 - step 3555: {'train/loss': 0.011155247688293457, 'train/grad_norm': 0.41282787919044495, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:45:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:45:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:26] train_sft.py:596 - step 3556: {'train/loss': 0.014921605587005615, 'train/grad_norm': 0.3903733789920807, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:45:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:31] train_sft.py:596 - step 3557: {'train/loss': 0.013283677399158478, 'train/grad_norm': 0.5648602247238159, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:45:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:45:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:35] train_sft.py:596 - step 3558: {'train/loss': 0.01497998833656311, 'train/grad_norm': 0.4259795546531677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:45:35] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:45:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:40] train_sft.py:596 - step 3559: {'train/loss': 0.019547253847122192, 'train/grad_norm': 0.38442379236221313, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:45:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:45] train_sft.py:596 - step 3560: {'train/loss': 0.011225536465644836, 'train/grad_norm': 0.322566419839859, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:45:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:49] train_sft.py:596 - step 3561: {'train/loss': 0.012204676866531372, 'train/grad_norm': 0.31017106771469116, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:45:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:54] train_sft.py:596 - step 3562: {'train/loss': 0.013362139463424683, 'train/grad_norm': 0.4052751660346985, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:45:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:45:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:45:59] train_sft.py:596 - step 3563: {'train/loss': 0.012892872095108032, 'train/grad_norm': 0.5893391370773315, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:45:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:45:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:04] train_sft.py:596 - step 3564: {'train/loss': 0.014950186014175415, 'train/grad_norm': 0.5775514841079712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:46:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:46:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:09] train_sft.py:596 - step 3565: {'train/loss': 0.009641826152801514, 'train/grad_norm': 0.46822589635849, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:46:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:46:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:13] train_sft.py:596 - step 3566: {'train/loss': 0.01852080225944519, 'train/grad_norm': 0.4703616797924042, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 16:46:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:46:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:18] train_sft.py:596 - step 3567: {'train/loss': 0.009451348334550858, 'train/grad_norm': 0.30373087525367737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:46:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:46:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:23] train_sft.py:596 - step 3568: {'train/loss': 0.008845679461956024, 'train/grad_norm': 0.3048436939716339, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:46:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:46:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:27] train_sft.py:596 - step 3569: {'train/loss': 0.013266880065202713, 'train/grad_norm': 0.3573547303676605, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:46:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:46:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:32] train_sft.py:596 - step 3570: {'train/loss': 0.012874580919742584, 'train/grad_norm': 0.3990321755409241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 16:46:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:46:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:36] train_sft.py:596 - step 3571: {'train/loss': 0.011236712336540222, 'train/grad_norm': 0.3944462537765503, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:46:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:46:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:41] train_sft.py:596 - step 3572: {'train/loss': 0.012998022139072418, 'train/grad_norm': 0.4175470471382141, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:46:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:46:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:46] train_sft.py:596 - step 3573: {'train/loss': 0.007444843649864197, 'train/grad_norm': 0.6646098494529724, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:46:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:46:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:51] train_sft.py:596 - step 3574: {'train/loss': 0.012173395603895187, 'train/grad_norm': 0.2046322524547577, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:46:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:46:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:46:55] train_sft.py:596 - step 3575: {'train/loss': 0.00784251093864441, 'train/grad_norm': 0.295846551656723, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:46:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:46:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:00] train_sft.py:596 - step 3576: {'train/loss': 0.022236347198486328, 'train/grad_norm': 0.6589226722717285, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:47:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:47:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:04] train_sft.py:596 - step 3577: {'train/loss': 0.01232805848121643, 'train/grad_norm': 0.46689319610595703, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:47:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:47:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:09] train_sft.py:596 - step 3578: {'train/loss': 0.012375269085168839, 'train/grad_norm': 0.388572633266449, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:47:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:47:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:14] train_sft.py:596 - step 3579: {'train/loss': 0.01649363711476326, 'train/grad_norm': 0.7878946661949158, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 16:47:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:47:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:19] train_sft.py:596 - step 3580: {'train/loss': 0.010802626609802246, 'train/grad_norm': 0.4062950313091278, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:47:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:47:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:24] train_sft.py:596 - step 3581: {'train/loss': 0.013261944055557251, 'train/grad_norm': 0.5968754887580872, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 16:47:24] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:47:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:29] train_sft.py:596 - step 3582: {'train/loss': 0.009221240878105164, 'train/grad_norm': 0.3483641743659973, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:47:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:47:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:34] train_sft.py:596 - step 3583: {'train/loss': 0.0161275714635849, 'train/grad_norm': 0.32118573784828186, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:47:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:47:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:38] train_sft.py:596 - step 3584: {'train/loss': 0.013296891003847122, 'train/grad_norm': 0.496451199054718, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:47:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:47:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:43] train_sft.py:596 - step 3585: {'train/loss': 0.013974949717521667, 'train/grad_norm': 0.6754530668258667, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:47:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:47:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:47] train_sft.py:596 - step 3586: {'train/loss': 0.01274106279015541, 'train/grad_norm': 0.4390104115009308, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:47:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:47:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:52] train_sft.py:596 - step 3587: {'train/loss': 0.017721563577651978, 'train/grad_norm': 0.4683319926261902, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 16:47:52] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:47:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:47:57] train_sft.py:596 - step 3588: {'train/loss': 0.02018039603717625, 'train/grad_norm': 0.6333509087562561, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:47:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:47:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:02] train_sft.py:596 - step 3589: {'train/loss': 0.01901295781135559, 'train/grad_norm': 0.725765585899353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 16:48:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:48:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:06] train_sft.py:596 - step 3590: {'train/loss': 0.01664350926876068, 'train/grad_norm': 0.48444700241088867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:48:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:48:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:11] train_sft.py:596 - step 3591: {'train/loss': 0.01137196272611618, 'train/grad_norm': 0.5197401642799377, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 16:48:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:48:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:16] train_sft.py:596 - step 3592: {'train/loss': 0.012462166603654623, 'train/grad_norm': 0.5921801328659058, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:48:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:48:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:20] train_sft.py:596 - step 3593: {'train/loss': 0.012292042374610901, 'train/grad_norm': 0.3986222445964813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 16:48:20] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:48:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:25] train_sft.py:596 - step 3594: {'train/loss': 0.01750124990940094, 'train/grad_norm': 0.5444346070289612, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 16:48:25] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:48:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:30] train_sft.py:596 - step 3595: {'train/loss': 0.020137448562309146, 'train/grad_norm': 0.5066924095153809, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:48:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:48:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:35] train_sft.py:596 - step 3596: {'train/loss': 0.01617126166820526, 'train/grad_norm': 0.2816946506500244, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:48:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:48:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:40] train_sft.py:596 - step 3597: {'train/loss': 0.01594008505344391, 'train/grad_norm': 0.2820183336734772, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:48:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:48:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:44] train_sft.py:596 - step 3598: {'train/loss': 0.014523670077323914, 'train/grad_norm': 0.5327188968658447, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:48:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:48:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:50] train_sft.py:596 - step 3599: {'train/loss': 0.013648787629790604, 'train/grad_norm': 0.26761192083358765, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  5.00s/it]
[2025-12-29 16:48:50] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:48:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:54] train_sft.py:596 - step 3600: {'train/loss': 0.014059362933039665, 'train/grad_norm': 0.3456687927246094, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 16:48:54] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:48:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:48:59] train_sft.py:596 - step 3601: {'train/loss': 0.015609368681907654, 'train/grad_norm': 0.6463789343833923, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:48:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:49:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:04] train_sft.py:596 - step 3602: {'train/loss': 0.014203295111656189, 'train/grad_norm': 0.41023531556129456, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:49:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:49:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:09] train_sft.py:596 - step 3603: {'train/loss': 0.020175166428089142, 'train/grad_norm': 0.9431673288345337, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:49:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:49:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:13] train_sft.py:596 - step 3604: {'train/loss': 0.008335400372743607, 'train/grad_norm': 0.2999354898929596, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:49:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:49:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:18] train_sft.py:596 - step 3605: {'train/loss': 0.013324078172445297, 'train/grad_norm': 0.540399432182312, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:49:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:49:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:23] train_sft.py:596 - step 3606: {'train/loss': 0.009564116597175598, 'train/grad_norm': 0.5997394323348999, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:49:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:49:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:27] train_sft.py:596 - step 3607: {'train/loss': 0.015347644686698914, 'train/grad_norm': 0.3592517673969269, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:49:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:49:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:32] train_sft.py:596 - step 3608: {'train/loss': 0.007891889661550522, 'train/grad_norm': 0.28336629271507263, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:49:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:49:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:37] train_sft.py:596 - step 3609: {'train/loss': 0.01149129867553711, 'train/grad_norm': 0.4299233853816986, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:49:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:49:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:42] train_sft.py:596 - step 3610: {'train/loss': 0.012794766575098038, 'train/grad_norm': 0.5109173655509949, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:49:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:49:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:46] train_sft.py:596 - step 3611: {'train/loss': 0.007230851799249649, 'train/grad_norm': 0.24125456809997559, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:49:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:49:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:51] train_sft.py:596 - step 3612: {'train/loss': 0.0102485790848732, 'train/grad_norm': 0.30769363045692444, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:49:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:49:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:49:56] train_sft.py:596 - step 3613: {'train/loss': 0.010360881686210632, 'train/grad_norm': 0.22267210483551025, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 16:49:56] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:49:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:01] train_sft.py:596 - step 3614: {'train/loss': 0.007917646318674088, 'train/grad_norm': 0.1837574988603592, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:50:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:50:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:05] train_sft.py:596 - step 3615: {'train/loss': 0.02795807272195816, 'train/grad_norm': 0.8233333826065063, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:50:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:50:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:10] train_sft.py:596 - step 3616: {'train/loss': 0.009410962462425232, 'train/grad_norm': 0.29522401094436646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:50:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:50:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:15] train_sft.py:596 - step 3617: {'train/loss': 0.01076766848564148, 'train/grad_norm': 0.41833409667015076, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:50:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:50:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:19] train_sft.py:596 - step 3618: {'train/loss': 0.007760820910334587, 'train/grad_norm': 0.24304650723934174, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:50:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:50:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:24] train_sft.py:596 - step 3619: {'train/loss': 0.00697646290063858, 'train/grad_norm': 0.3388826549053192, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:50:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:50:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:29] train_sft.py:596 - step 3620: {'train/loss': 0.01579800248146057, 'train/grad_norm': 0.46754077076911926, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:50:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:50:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:33] train_sft.py:596 - step 3621: {'train/loss': 0.01470531802624464, 'train/grad_norm': 0.6702247858047485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:50:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:50:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:38] train_sft.py:596 - step 3622: {'train/loss': 0.008645465597510338, 'train/grad_norm': 0.4181038737297058, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:50:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:50:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:43] train_sft.py:596 - step 3623: {'train/loss': 0.014800034463405609, 'train/grad_norm': 0.24632389843463898, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:50:43] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:50:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:48] train_sft.py:596 - step 3624: {'train/loss': 0.012061119079589844, 'train/grad_norm': 0.4810565412044525, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:50:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:50:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:53] train_sft.py:596 - step 3625: {'train/loss': 0.009576350450515747, 'train/grad_norm': 0.23217646777629852, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:50:53] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:50:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:50:57] train_sft.py:596 - step 3626: {'train/loss': 0.007322888821363449, 'train/grad_norm': 0.254390686750412, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:50:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:50:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:02] train_sft.py:596 - step 3627: {'train/loss': 0.012391090393066406, 'train/grad_norm': 0.46767207980155945, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:51:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:51:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:07] train_sft.py:596 - step 3628: {'train/loss': 0.01842448115348816, 'train/grad_norm': 0.6888648271560669, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 16:51:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:51:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:12] train_sft.py:596 - step 3629: {'train/loss': 0.012575171887874603, 'train/grad_norm': 0.292314738035202, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 16:51:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:51:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:17] train_sft.py:596 - step 3630: {'train/loss': 0.00961374957114458, 'train/grad_norm': 0.29101040959358215, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:51:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:51:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:22] train_sft.py:596 - step 3631: {'train/loss': 0.014145785477012396, 'train/grad_norm': 0.5918800830841064, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 16:51:22] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:51:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:27] train_sft.py:596 - step 3632: {'train/loss': 0.008357539772987366, 'train/grad_norm': 0.4462510347366333, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:51:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:51:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:31] train_sft.py:596 - step 3633: {'train/loss': 0.008939113467931747, 'train/grad_norm': 0.34373384714126587, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:51:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:51:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:36] train_sft.py:596 - step 3634: {'train/loss': 0.01624995470046997, 'train/grad_norm': 0.3724921643733978, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:51:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:51:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:41] train_sft.py:596 - step 3635: {'train/loss': 0.004761099815368652, 'train/grad_norm': 0.15519407391548157, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:51:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:51:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:45] train_sft.py:596 - step 3636: {'train/loss': 0.006201934069395065, 'train/grad_norm': 0.30413174629211426, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 16:51:45] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:51:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:50] train_sft.py:596 - step 3637: {'train/loss': 0.025601640343666077, 'train/grad_norm': 0.5566479563713074, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:51:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:51:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:55] train_sft.py:596 - step 3638: {'train/loss': 0.011171121150255203, 'train/grad_norm': 0.19346670806407928, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:51:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:51:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:51:59] train_sft.py:596 - step 3639: {'train/loss': 0.015282247215509415, 'train/grad_norm': 0.46417132019996643, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:51:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:51:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:05] train_sft.py:596 - step 3640: {'train/loss': 0.007560521364212036, 'train/grad_norm': 0.2734324038028717, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.18s/it]
[2025-12-29 16:52:05] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 16:52:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:09] train_sft.py:596 - step 3641: {'train/loss': 0.014718115329742432, 'train/grad_norm': 0.8135932683944702, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:52:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:52:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:14] train_sft.py:596 - step 3642: {'train/loss': 0.00945192575454712, 'train/grad_norm': 0.368782639503479, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:52:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:52:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:19] train_sft.py:596 - step 3643: {'train/loss': 0.008198946714401245, 'train/grad_norm': 0.23521049320697784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:52:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:52:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:24] train_sft.py:596 - step 3644: {'train/loss': 0.009887456893920898, 'train/grad_norm': 0.23056478798389435, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:52:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:52:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:28] train_sft.py:596 - step 3645: {'train/loss': 0.006381388753652573, 'train/grad_norm': 0.19626793265342712, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:52:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:52:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:33] train_sft.py:596 - step 3646: {'train/loss': 0.012316565494984388, 'train/grad_norm': 0.4655350148677826, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:52:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:52:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:37] train_sft.py:596 - step 3647: {'train/loss': 0.008670121431350708, 'train/grad_norm': 0.2558062970638275, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:52:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:52:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:42] train_sft.py:596 - step 3648: {'train/loss': 0.019176512956619263, 'train/grad_norm': 0.3486093580722809, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:52:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:52:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:47] train_sft.py:596 - step 3649: {'train/loss': 0.009100645780563354, 'train/grad_norm': 0.22459252178668976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 16:52:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:52:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:52] train_sft.py:596 - step 3650: {'train/loss': 0.011669419705867767, 'train/grad_norm': 0.4153561592102051, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:52:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:52:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:52:56] train_sft.py:596 - step 3651: {'train/loss': 0.008674755692481995, 'train/grad_norm': 0.17791323363780975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:52:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:52:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:01] train_sft.py:596 - step 3652: {'train/loss': 0.010235697031021118, 'train/grad_norm': 0.4627510905265808, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:53:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:53:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:05] train_sft.py:596 - step 3653: {'train/loss': 0.02883298695087433, 'train/grad_norm': 0.6571778655052185, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:53:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:53:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:10] train_sft.py:596 - step 3654: {'train/loss': 0.005166005343198776, 'train/grad_norm': 0.23645293712615967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:53:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:53:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:15] train_sft.py:596 - step 3655: {'train/loss': 0.011646827682852745, 'train/grad_norm': 0.45463863015174866, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:53:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:53:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:19] train_sft.py:596 - step 3656: {'train/loss': 0.01152493804693222, 'train/grad_norm': 0.21048083901405334, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:53:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:53:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:24] train_sft.py:596 - step 3657: {'train/loss': 0.010421723127365112, 'train/grad_norm': 0.42203933000564575, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 16:53:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:53:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:29] train_sft.py:596 - step 3658: {'train/loss': 0.01550186239182949, 'train/grad_norm': 0.6639852523803711, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:53:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:53:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:34] train_sft.py:596 - step 3659: {'train/loss': 0.009165693074464798, 'train/grad_norm': 0.2985415458679199, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:53:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:53:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:39] train_sft.py:596 - step 3660: {'train/loss': 0.010994281619787216, 'train/grad_norm': 0.5556449294090271, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:53:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:53:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:44] train_sft.py:596 - step 3661: {'train/loss': 0.017927348613739014, 'train/grad_norm': 0.6556106805801392, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 16:53:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:53:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:48] train_sft.py:596 - step 3662: {'train/loss': 0.015191495418548584, 'train/grad_norm': 0.43352633714675903, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:53:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:53:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:53] train_sft.py:596 - step 3663: {'train/loss': 0.014900170266628265, 'train/grad_norm': 0.5389683246612549, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:53:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:53:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:53:58] train_sft.py:596 - step 3664: {'train/loss': 0.015020512044429779, 'train/grad_norm': 0.23461109399795532, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:53:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:53:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:02] train_sft.py:596 - step 3665: {'train/loss': 0.0204024538397789, 'train/grad_norm': 0.3646538257598877, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 16:54:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:54:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:07] train_sft.py:596 - step 3666: {'train/loss': 0.012656837701797485, 'train/grad_norm': 0.45299437642097473, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:54:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:54:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:12] train_sft.py:596 - step 3667: {'train/loss': 0.009590595960617065, 'train/grad_norm': 0.3743818402290344, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:54:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:54:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:17] train_sft.py:596 - step 3668: {'train/loss': 0.016112452372908592, 'train/grad_norm': 0.529319703578949, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:54:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:54:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:21] train_sft.py:596 - step 3669: {'train/loss': 0.009086132049560547, 'train/grad_norm': 0.30272331833839417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:54:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:54:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:26] train_sft.py:596 - step 3670: {'train/loss': 0.0149930939078331, 'train/grad_norm': 0.4878031611442566, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:54:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:54:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:31] train_sft.py:596 - step 3671: {'train/loss': 0.012742668390274048, 'train/grad_norm': 0.447653591632843, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:54:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:54:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:36] train_sft.py:596 - step 3672: {'train/loss': 0.015030130743980408, 'train/grad_norm': 0.33826014399528503, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:54:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:54:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:40] train_sft.py:596 - step 3673: {'train/loss': 0.011213302612304688, 'train/grad_norm': 0.35397571325302124, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 16:54:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:54:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:45] train_sft.py:596 - step 3674: {'train/loss': 0.014921844005584717, 'train/grad_norm': 0.31415343284606934, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 16:54:45] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:54:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:50] train_sft.py:596 - step 3675: {'train/loss': 0.012164998799562454, 'train/grad_norm': 0.38096752762794495, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:54:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:54:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:54] train_sft.py:596 - step 3676: {'train/loss': 0.01885277032852173, 'train/grad_norm': 0.6001443862915039, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:54:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:54:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:54:59] train_sft.py:596 - step 3677: {'train/loss': 0.024080827832221985, 'train/grad_norm': 0.7133452892303467, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 16:54:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:55:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:05] train_sft.py:596 - step 3678: {'train/loss': 0.01002742350101471, 'train/grad_norm': 0.1911848932504654, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 16:55:05] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:55:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:09] train_sft.py:596 - step 3679: {'train/loss': 0.009808771312236786, 'train/grad_norm': 0.2468269318342209, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 16:55:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:55:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:14] train_sft.py:596 - step 3680: {'train/loss': 0.009824708104133606, 'train/grad_norm': 0.37815508246421814, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 16:55:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:55:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:19] train_sft.py:596 - step 3681: {'train/loss': 0.010080158710479736, 'train/grad_norm': 0.2626590132713318, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 16:55:19] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:55:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:24] train_sft.py:596 - step 3682: {'train/loss': 0.013249441981315613, 'train/grad_norm': 0.4715210497379303, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 16:55:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:55:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:29] train_sft.py:596 - step 3683: {'train/loss': 0.012169711291790009, 'train/grad_norm': 0.3407823443412781, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 16:55:29] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:55:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:34] train_sft.py:596 - step 3684: {'train/loss': 0.009851217269897461, 'train/grad_norm': 0.25982630252838135, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 16:55:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:55:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:39] train_sft.py:596 - step 3685: {'train/loss': 0.015217110514640808, 'train/grad_norm': 0.3965170085430145, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 16:55:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:55:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:43] train_sft.py:596 - step 3686: {'train/loss': 0.009593125432729721, 'train/grad_norm': 0.4386291801929474, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:55:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:55:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:48] train_sft.py:596 - step 3687: {'train/loss': 0.0069239139556884766, 'train/grad_norm': 0.211353600025177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:55:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:55:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:53] train_sft.py:596 - step 3688: {'train/loss': 0.015006398782134056, 'train/grad_norm': 0.3409661054611206, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:55:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:55:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:55:58] train_sft.py:596 - step 3689: {'train/loss': 0.01784650981426239, 'train/grad_norm': 0.4544944167137146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 16:55:58] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 16:55:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:03] train_sft.py:596 - step 3690: {'train/loss': 0.012119829654693604, 'train/grad_norm': 1.004189372062683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:56:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:56:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:08] train_sft.py:596 - step 3691: {'train/loss': 0.017482846975326538, 'train/grad_norm': 0.4760766327381134, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 16:56:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:56:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:13] train_sft.py:596 - step 3692: {'train/loss': 0.008282145485281944, 'train/grad_norm': 0.26412031054496765, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 16:56:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:56:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:18] train_sft.py:596 - step 3693: {'train/loss': 0.008435681462287903, 'train/grad_norm': 0.33989882469177246, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 16:56:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:56:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:23] train_sft.py:596 - step 3694: {'train/loss': 0.008631184697151184, 'train/grad_norm': 0.2854796051979065, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 16:56:23] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:56:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:28] train_sft.py:596 - step 3695: {'train/loss': 0.01562681794166565, 'train/grad_norm': 0.3578856885433197, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 16:56:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:56:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:32] train_sft.py:596 - step 3696: {'train/loss': 0.013915792107582092, 'train/grad_norm': 0.4665069878101349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 16:56:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:56:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:37] train_sft.py:596 - step 3697: {'train/loss': 0.01853390969336033, 'train/grad_norm': 0.9505194425582886, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:56:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:56:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:42] train_sft.py:596 - step 3698: {'train/loss': 0.012808933854103088, 'train/grad_norm': 0.3539730906486511, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:56:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:56:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:47] train_sft.py:596 - step 3699: {'train/loss': 0.02486711833626032, 'train/grad_norm': 0.6981000304222107, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:56:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:56:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:52] train_sft.py:596 - step 3700: {'train/loss': 0.017178349196910858, 'train/grad_norm': 0.43992990255355835, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:56:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:56:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:56:56] train_sft.py:596 - step 3701: {'train/loss': 0.01805834472179413, 'train/grad_norm': 0.48019444942474365, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:56:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:56:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:01] train_sft.py:596 - step 3702: {'train/loss': 0.014325723052024841, 'train/grad_norm': 0.45682987570762634, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 16:57:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:57:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:06] train_sft.py:596 - step 3703: {'train/loss': 0.020345300436019897, 'train/grad_norm': 0.5643133521080017, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:57:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:57:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:10] train_sft.py:596 - step 3704: {'train/loss': 0.013774901628494263, 'train/grad_norm': 0.4498957395553589, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 16:57:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:57:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:15] train_sft.py:596 - step 3705: {'train/loss': 0.0074836015701293945, 'train/grad_norm': 0.32953670620918274, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:57:15] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:57:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:20] train_sft.py:596 - step 3706: {'train/loss': 0.010827318299561739, 'train/grad_norm': 0.3301877975463867, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 16:57:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:57:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:24] train_sft.py:596 - step 3707: {'train/loss': 0.018815070390701294, 'train/grad_norm': 0.6044511198997498, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 16:57:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:57:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:29] train_sft.py:596 - step 3708: {'train/loss': 0.014889374375343323, 'train/grad_norm': 0.48558852076530457, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 16:57:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:57:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:34] train_sft.py:596 - step 3709: {'train/loss': 0.014327093958854675, 'train/grad_norm': 0.41270723938941956, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 16:57:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:57:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:39] train_sft.py:596 - step 3710: {'train/loss': 0.015074953436851501, 'train/grad_norm': 0.472705215215683, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 16:57:39] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 16:57:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:43] train_sft.py:596 - step 3711: {'train/loss': 0.009686559438705444, 'train/grad_norm': 0.47426408529281616, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:57:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:57:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:48] train_sft.py:596 - step 3712: {'train/loss': 0.01638520509004593, 'train/grad_norm': 0.4868675470352173, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:57:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:57:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:53] train_sft.py:596 - step 3713: {'train/loss': 0.015198230743408203, 'train/grad_norm': 0.3440694808959961, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:57:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:57:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:57:58] train_sft.py:596 - step 3714: {'train/loss': 0.006353914737701416, 'train/grad_norm': 0.14732569456100464, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 16:57:58] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 16:57:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:02] train_sft.py:596 - step 3715: {'train/loss': 0.013758555054664612, 'train/grad_norm': 0.36619654297828674, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:58:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:58:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:07] train_sft.py:596 - step 3716: {'train/loss': 0.015945836901664734, 'train/grad_norm': 0.39496049284935, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 16:58:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:58:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:11] train_sft.py:596 - step 3717: {'train/loss': 0.008967012166976929, 'train/grad_norm': 0.2970461845397949, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 16:58:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:58:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:17] train_sft.py:596 - step 3718: {'train/loss': 0.01574631780385971, 'train/grad_norm': 0.46240508556365967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 16:58:17] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 16:58:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:21] train_sft.py:596 - step 3719: {'train/loss': 0.014968298375606537, 'train/grad_norm': 0.4232407212257385, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 16:58:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:58:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:26] train_sft.py:596 - step 3720: {'train/loss': 0.011169500648975372, 'train/grad_norm': 0.34935301542282104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 16:58:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:58:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:31] train_sft.py:596 - step 3721: {'train/loss': 0.010842159390449524, 'train/grad_norm': 0.36312708258628845, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:58:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:58:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:36] train_sft.py:596 - step 3722: {'train/loss': 0.013204231858253479, 'train/grad_norm': 0.4564972519874573, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:58:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:58:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:41] train_sft.py:596 - step 3723: {'train/loss': 0.012417938560247421, 'train/grad_norm': 0.3351303040981293, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:58:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:58:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:45] train_sft.py:596 - step 3724: {'train/loss': 0.01083509624004364, 'train/grad_norm': 0.600357711315155, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:58:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:58:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:50] train_sft.py:596 - step 3725: {'train/loss': 0.014262273907661438, 'train/grad_norm': 0.4136453866958618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:58:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:58:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:55] train_sft.py:596 - step 3726: {'train/loss': 0.014059320092201233, 'train/grad_norm': 0.25729140639305115, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 16:58:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:58:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:58:59] train_sft.py:596 - step 3727: {'train/loss': 0.011331677436828613, 'train/grad_norm': 0.3694206178188324, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:58:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:59:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:04] train_sft.py:596 - step 3728: {'train/loss': 0.01843169331550598, 'train/grad_norm': 0.3444686532020569, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:59:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:59:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:09] train_sft.py:596 - step 3729: {'train/loss': 0.011862605810165405, 'train/grad_norm': 0.5425689220428467, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 16:59:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:59:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:14] train_sft.py:596 - step 3730: {'train/loss': 0.011358365416526794, 'train/grad_norm': 0.3353729546070099, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 16:59:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:59:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:19] train_sft.py:596 - step 3731: {'train/loss': 0.01586940884590149, 'train/grad_norm': 0.5475106835365295, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:59:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:59:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:24] train_sft.py:596 - step 3732: {'train/loss': 0.011599689722061157, 'train/grad_norm': 2.0508639812469482, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 16:59:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:59:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:28] train_sft.py:596 - step 3733: {'train/loss': 0.013271503150463104, 'train/grad_norm': 0.5418760180473328, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 16:59:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:59:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:33] train_sft.py:596 - step 3734: {'train/loss': 0.008155003190040588, 'train/grad_norm': 0.39881202578544617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 16:59:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:59:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:38] train_sft.py:596 - step 3735: {'train/loss': 0.008617661893367767, 'train/grad_norm': 0.17882908880710602, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 16:59:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 16:59:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:42] train_sft.py:596 - step 3736: {'train/loss': 0.01036563515663147, 'train/grad_norm': 0.37739160656929016, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 16:59:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 16:59:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:47] train_sft.py:596 - step 3737: {'train/loss': 0.012041106820106506, 'train/grad_norm': 0.38894355297088623, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 16:59:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:59:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:52] train_sft.py:596 - step 3738: {'train/loss': 0.012407921254634857, 'train/grad_norm': 0.3477385342121124, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 16:59:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 16:59:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 16:59:57] train_sft.py:596 - step 3739: {'train/loss': 0.02009713649749756, 'train/grad_norm': 0.7811471223831177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 16:59:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 16:59:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:01] train_sft.py:596 - step 3740: {'train/loss': 0.010111287236213684, 'train/grad_norm': 0.6052369475364685, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:00:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:00:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:06] train_sft.py:596 - step 3741: {'train/loss': 0.013911187648773193, 'train/grad_norm': 0.5526461601257324, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:00:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:00:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:11] train_sft.py:596 - step 3742: {'train/loss': 0.015124667435884476, 'train/grad_norm': 0.35174182057380676, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:00:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:00:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:15] train_sft.py:596 - step 3743: {'train/loss': 0.010268840938806534, 'train/grad_norm': 0.2930430769920349, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:00:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:00:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:20] train_sft.py:596 - step 3744: {'train/loss': 0.013671793043613434, 'train/grad_norm': 0.34642836451530457, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 17:00:20] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:00:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:25] train_sft.py:596 - step 3745: {'train/loss': 0.010884314775466919, 'train/grad_norm': 0.21315956115722656, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 17:00:25] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:00:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:30] train_sft.py:596 - step 3746: {'train/loss': 0.015513129532337189, 'train/grad_norm': 0.4728739857673645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:00:30] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:00:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:35] train_sft.py:596 - step 3747: {'train/loss': 0.0069086551666259766, 'train/grad_norm': 0.13462410867214203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:00:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:00:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:39] train_sft.py:596 - step 3748: {'train/loss': 0.014200631529092789, 'train/grad_norm': 0.4084775447845459, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:00:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:00:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:44] train_sft.py:596 - step 3749: {'train/loss': 0.012934967875480652, 'train/grad_norm': 0.3864078223705292, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:00:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:00:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:49] train_sft.py:596 - step 3750: {'train/loss': 0.012288078665733337, 'train/grad_norm': 0.4433940351009369, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:00:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:00:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:53] train_sft.py:596 - step 3751: {'train/loss': 0.006561581045389175, 'train/grad_norm': 0.38090017437934875, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:00:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:00:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:00:58] train_sft.py:596 - step 3752: {'train/loss': 0.01367291808128357, 'train/grad_norm': 0.3868809938430786, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:00:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:00:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:03] train_sft.py:596 - step 3753: {'train/loss': 0.016610249876976013, 'train/grad_norm': 0.4732092022895813, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:01:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:01:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:08] train_sft.py:596 - step 3754: {'train/loss': 0.017244398593902588, 'train/grad_norm': 0.5771744847297668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:01:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:01:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:12] train_sft.py:596 - step 3755: {'train/loss': 0.021301530301570892, 'train/grad_norm': 0.4409157633781433, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:01:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:01:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:17] train_sft.py:596 - step 3756: {'train/loss': 0.01754792407155037, 'train/grad_norm': 0.5338082909584045, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:01:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:01:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:22] train_sft.py:596 - step 3757: {'train/loss': 0.009605884552001953, 'train/grad_norm': 0.4345199167728424, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:01:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:01:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:26] train_sft.py:596 - step 3758: {'train/loss': 0.00991523265838623, 'train/grad_norm': 0.38221147656440735, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:01:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:01:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:31] train_sft.py:596 - step 3759: {'train/loss': 0.018486805260181427, 'train/grad_norm': 0.6117954850196838, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:01:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:01:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:36] train_sft.py:596 - step 3760: {'train/loss': 0.008213639259338379, 'train/grad_norm': 0.27652642130851746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:01:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:01:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:41] train_sft.py:596 - step 3761: {'train/loss': 0.01443970575928688, 'train/grad_norm': 0.35128724575042725, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 17:01:41] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:01:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:46] train_sft.py:596 - step 3762: {'train/loss': 0.010999426245689392, 'train/grad_norm': 0.3077847957611084, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 17:01:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:01:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:50] train_sft.py:596 - step 3763: {'train/loss': 0.013456150889396667, 'train/grad_norm': 0.3565184772014618, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:01:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:01:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:01:56] train_sft.py:596 - step 3764: {'train/loss': 0.010022921487689018, 'train/grad_norm': 0.5327434539794922, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 17:01:56] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:01:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:00] train_sft.py:596 - step 3765: {'train/loss': 0.021737299859523773, 'train/grad_norm': 0.41168197989463806, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:02:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:02:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:05] train_sft.py:596 - step 3766: {'train/loss': 0.011909708380699158, 'train/grad_norm': 0.6361287832260132, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:02:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:02:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:10] train_sft.py:596 - step 3767: {'train/loss': 0.008768081665039062, 'train/grad_norm': 0.3149893879890442, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:02:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:02:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:14] train_sft.py:596 - step 3768: {'train/loss': 0.016058988869190216, 'train/grad_norm': 0.5028054714202881, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:02:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:02:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:19] train_sft.py:596 - step 3769: {'train/loss': 0.007524967193603516, 'train/grad_norm': 0.2078331708908081, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:02:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:02:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:24] train_sft.py:596 - step 3770: {'train/loss': 0.010469619184732437, 'train/grad_norm': 0.2968381941318512, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:02:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:02:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:29] train_sft.py:596 - step 3771: {'train/loss': 0.018981456756591797, 'train/grad_norm': 0.6236511468887329, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 17:02:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:02:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:33] train_sft.py:596 - step 3772: {'train/loss': 0.01720207929611206, 'train/grad_norm': 0.6624327301979065, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:02:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:02:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:38] train_sft.py:596 - step 3773: {'train/loss': 0.010624252259731293, 'train/grad_norm': 0.22970148921012878, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:02:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:02:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:43] train_sft.py:596 - step 3774: {'train/loss': 0.015430808067321777, 'train/grad_norm': 0.5021567940711975, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:02:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:02:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:48] train_sft.py:596 - step 3775: {'train/loss': 0.01750236749649048, 'train/grad_norm': 0.4518548250198364, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 17:02:48] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:02:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:52] train_sft.py:596 - step 3776: {'train/loss': 0.017831146717071533, 'train/grad_norm': 0.40990495681762695, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 17:02:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:02:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:02:57] train_sft.py:596 - step 3777: {'train/loss': 0.010266780853271484, 'train/grad_norm': 0.36425378918647766, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:02:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:02:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:02] train_sft.py:596 - step 3778: {'train/loss': 0.014108732342720032, 'train/grad_norm': 0.3687695562839508, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:03:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:03:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:06] train_sft.py:596 - step 3779: {'train/loss': 0.015513598918914795, 'train/grad_norm': 0.3796481192111969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 17:03:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:03:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:11] train_sft.py:596 - step 3780: {'train/loss': 0.015902053099125624, 'train/grad_norm': 0.33924606442451477, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:03:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:03:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:16] train_sft.py:596 - step 3781: {'train/loss': 0.01203838735818863, 'train/grad_norm': 0.3504512906074524, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 17:03:16] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:03:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:21] train_sft.py:596 - step 3782: {'train/loss': 0.0154915452003479, 'train/grad_norm': 0.648381233215332, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:03:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:03:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:26] train_sft.py:596 - step 3783: {'train/loss': 0.012501410208642483, 'train/grad_norm': 0.4327335059642792, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:03:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:03:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:31] train_sft.py:596 - step 3784: {'train/loss': 0.013448745012283325, 'train/grad_norm': 0.26507115364074707, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:03:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:03:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:36] train_sft.py:596 - step 3785: {'train/loss': 0.011690730229020119, 'train/grad_norm': 0.46970611810684204, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 17:03:36] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:03:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:41] train_sft.py:596 - step 3786: {'train/loss': 0.017130479216575623, 'train/grad_norm': 0.905066967010498, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:03:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:03:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:45] train_sft.py:596 - step 3787: {'train/loss': 0.014695830643177032, 'train/grad_norm': 0.5203804969787598, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:03:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:03:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:50] train_sft.py:596 - step 3788: {'train/loss': 0.012043923139572144, 'train/grad_norm': 0.30955076217651367, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:03:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:03:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:54] train_sft.py:596 - step 3789: {'train/loss': 0.017728418111801147, 'train/grad_norm': 0.4133988916873932, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:03:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:03:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:03:59] train_sft.py:596 - step 3790: {'train/loss': 0.014774829149246216, 'train/grad_norm': 0.46518605947494507, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:03:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:03:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:04] train_sft.py:596 - step 3791: {'train/loss': 0.009093120694160461, 'train/grad_norm': 0.2557891309261322, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:04:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:04:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:09] train_sft.py:596 - step 3792: {'train/loss': 0.014465034008026123, 'train/grad_norm': 0.39761242270469666, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:04:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:04:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:13] train_sft.py:596 - step 3793: {'train/loss': 0.013679184019565582, 'train/grad_norm': 0.32268694043159485, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:04:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:04:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:18] train_sft.py:596 - step 3794: {'train/loss': 0.010984323918819427, 'train/grad_norm': 0.3585270643234253, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:04:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:04:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:23] train_sft.py:596 - step 3795: {'train/loss': 0.010878831148147583, 'train/grad_norm': 0.3832147717475891, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:04:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:04:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:27] train_sft.py:596 - step 3796: {'train/loss': 0.010565556585788727, 'train/grad_norm': 0.3405352532863617, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 17:04:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:04:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:32] train_sft.py:596 - step 3797: {'train/loss': 0.018945377320051193, 'train/grad_norm': 0.41941946744918823, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 17:04:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:04:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:37] train_sft.py:596 - step 3798: {'train/loss': 0.024680837988853455, 'train/grad_norm': 0.5867099165916443, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 17:04:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:04:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:42] train_sft.py:596 - step 3799: {'train/loss': 0.011546090245246887, 'train/grad_norm': 0.33813947439193726, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:04:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:04:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:47] train_sft.py:596 - step 3800: {'train/loss': 0.016264736652374268, 'train/grad_norm': 0.4165358543395996, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:04:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:04:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:51] train_sft.py:596 - step 3801: {'train/loss': 0.014375776052474976, 'train/grad_norm': 0.30503687262535095, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 17:04:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:04:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:04:56] train_sft.py:596 - step 3802: {'train/loss': 0.006946444511413574, 'train/grad_norm': 0.26408493518829346, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:04:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:04:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:01] train_sft.py:596 - step 3803: {'train/loss': 0.013454854488372803, 'train/grad_norm': 0.6321786642074585, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:05:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:05:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:06] train_sft.py:596 - step 3804: {'train/loss': 0.011372406035661697, 'train/grad_norm': 0.2483554482460022, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:05:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:05:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:11] train_sft.py:596 - step 3805: {'train/loss': 0.012286901473999023, 'train/grad_norm': 0.38542672991752625, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 17:05:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:05:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:15] train_sft.py:596 - step 3806: {'train/loss': 0.011777032166719437, 'train/grad_norm': 0.40163129568099976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:05:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:05:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:20] train_sft.py:596 - step 3807: {'train/loss': 0.016500920057296753, 'train/grad_norm': 0.24635276198387146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:05:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:05:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:24] train_sft.py:596 - step 3808: {'train/loss': 0.01409541442990303, 'train/grad_norm': 0.3775392174720764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:05:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:05:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:29] train_sft.py:596 - step 3809: {'train/loss': 0.00926734134554863, 'train/grad_norm': 0.4288971424102783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 17:05:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:05:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:33] train_sft.py:596 - step 3810: {'train/loss': 0.012188315391540527, 'train/grad_norm': 0.31913039088249207, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:05:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:05:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:38] train_sft.py:596 - step 3811: {'train/loss': 0.021079212427139282, 'train/grad_norm': 0.42823660373687744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:05:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:05:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:43] train_sft.py:596 - step 3812: {'train/loss': 0.013997465372085571, 'train/grad_norm': 0.5357087254524231, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:05:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:05:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:48] train_sft.py:596 - step 3813: {'train/loss': 0.013118047267198563, 'train/grad_norm': 0.4255249500274658, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:05:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:05:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:52] train_sft.py:596 - step 3814: {'train/loss': 0.009840980172157288, 'train/grad_norm': 0.23290127515792847, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:05:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:05:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:05:57] train_sft.py:596 - step 3815: {'train/loss': 0.005831986665725708, 'train/grad_norm': 0.258229523897171, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 17:05:57] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:05:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:02] train_sft.py:596 - step 3816: {'train/loss': 0.011458292603492737, 'train/grad_norm': 0.2821127772331238, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 17:06:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:06:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:07] train_sft.py:596 - step 3817: {'train/loss': 0.02308560162782669, 'train/grad_norm': 0.6460157632827759, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:06:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:06:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:11] train_sft.py:596 - step 3818: {'train/loss': 0.014041893184185028, 'train/grad_norm': 0.3754957318305969, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:06:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:06:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:16] train_sft.py:596 - step 3819: {'train/loss': 0.009966149926185608, 'train/grad_norm': 0.3818560540676117, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:06:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:06:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:21] train_sft.py:596 - step 3820: {'train/loss': 0.017654143273830414, 'train/grad_norm': 0.5556463003158569, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:06:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:06:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:25] train_sft.py:596 - step 3821: {'train/loss': 0.011565834283828735, 'train/grad_norm': 0.5326703786849976, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:06:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:06:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:30] train_sft.py:596 - step 3822: {'train/loss': 0.008830159902572632, 'train/grad_norm': 0.24456027150154114, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:06:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:06:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:35] train_sft.py:596 - step 3823: {'train/loss': 0.019683055579662323, 'train/grad_norm': 0.4102208912372589, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:06:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:06:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:39] train_sft.py:596 - step 3824: {'train/loss': 0.00991445779800415, 'train/grad_norm': 0.24991430342197418, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:06:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:06:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:45] train_sft.py:596 - step 3825: {'train/loss': 0.01275894045829773, 'train/grad_norm': 0.3845495283603668, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 17:06:45] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:06:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:50] train_sft.py:596 - step 3826: {'train/loss': 0.014898955821990967, 'train/grad_norm': 0.5389479398727417, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 17:06:50] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:06:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:06:55] train_sft.py:596 - step 3827: {'train/loss': 0.016394734382629395, 'train/grad_norm': 0.5530007481575012, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 17:06:55] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:06:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:00] train_sft.py:596 - step 3828: {'train/loss': 0.009677443653345108, 'train/grad_norm': 0.29516446590423584, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:07:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:04] train_sft.py:596 - step 3829: {'train/loss': 0.010529406368732452, 'train/grad_norm': 0.3446166217327118, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:07:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:07:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:09] train_sft.py:596 - step 3830: {'train/loss': 0.00925900787115097, 'train/grad_norm': 0.3529549539089203, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:07:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:07:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:14] train_sft.py:596 - step 3831: {'train/loss': 0.009498246014118195, 'train/grad_norm': 0.3309739828109741, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:07:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:18] train_sft.py:596 - step 3832: {'train/loss': 0.008913267403841019, 'train/grad_norm': 0.37530067563056946, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:07:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:07:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:23] train_sft.py:596 - step 3833: {'train/loss': 0.00631873682141304, 'train/grad_norm': 0.22502301633358002, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:07:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:28] train_sft.py:596 - step 3834: {'train/loss': 0.016589712351560593, 'train/grad_norm': 0.6194563508033752, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:07:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:32] train_sft.py:596 - step 3835: {'train/loss': 0.013547264039516449, 'train/grad_norm': 0.43164950609207153, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:07:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:37] train_sft.py:596 - step 3836: {'train/loss': 0.01066318154335022, 'train/grad_norm': 0.4679678976535797, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:07:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:41] train_sft.py:596 - step 3837: {'train/loss': 0.012457698583602905, 'train/grad_norm': 0.2986283600330353, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:07:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:07:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:46] train_sft.py:596 - step 3838: {'train/loss': 0.007803522050380707, 'train/grad_norm': 0.2139056921005249, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:07:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:51] train_sft.py:596 - step 3839: {'train/loss': 0.01232251524925232, 'train/grad_norm': 0.24751873314380646, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:07:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:07:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:07:55] train_sft.py:596 - step 3840: {'train/loss': 0.013284280896186829, 'train/grad_norm': 0.38230106234550476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:07:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:07:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:00] train_sft.py:596 - step 3841: {'train/loss': 0.01496802270412445, 'train/grad_norm': 0.32398721575737, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 17:08:01] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 17:08:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:05] train_sft.py:596 - step 3842: {'train/loss': 0.003819778561592102, 'train/grad_norm': 0.11493898183107376, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:08:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:08:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:10] train_sft.py:596 - step 3843: {'train/loss': 0.005659536458551884, 'train/grad_norm': 0.2520272135734558, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:08:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:08:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:15] train_sft.py:596 - step 3844: {'train/loss': 0.01872318983078003, 'train/grad_norm': 0.3777298927307129, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:08:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:08:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:19] train_sft.py:596 - step 3845: {'train/loss': 0.009587891399860382, 'train/grad_norm': 0.42740127444267273, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:08:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:08:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:24] train_sft.py:596 - step 3846: {'train/loss': 0.005170658230781555, 'train/grad_norm': 0.15471255779266357, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:08:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:08:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:29] train_sft.py:596 - step 3847: {'train/loss': 0.008853279054164886, 'train/grad_norm': 0.4278116226196289, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:08:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:08:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:33] train_sft.py:596 - step 3848: {'train/loss': 0.010516084730625153, 'train/grad_norm': 0.2896926701068878, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:08:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:08:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:38] train_sft.py:596 - step 3849: {'train/loss': 0.019885443150997162, 'train/grad_norm': 0.5976369976997375, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:08:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:08:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:43] train_sft.py:596 - step 3850: {'train/loss': 0.005296926945447922, 'train/grad_norm': 0.1698010265827179, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:08:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:08:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:47] train_sft.py:596 - step 3851: {'train/loss': 0.007332134991884232, 'train/grad_norm': 0.23584118485450745, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:08:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:08:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:52] train_sft.py:596 - step 3852: {'train/loss': 0.016154764220118523, 'train/grad_norm': 0.44271498918533325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:08:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:08:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:08:57] train_sft.py:596 - step 3853: {'train/loss': 0.011154502630233765, 'train/grad_norm': 0.40341243147850037, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:08:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:08:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:02] train_sft.py:596 - step 3854: {'train/loss': 0.012075066566467285, 'train/grad_norm': 0.36305132508277893, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 17:09:02] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:09:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:07] train_sft.py:596 - step 3855: {'train/loss': 0.016629427671432495, 'train/grad_norm': 0.5184927582740784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:09:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:09:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:11] train_sft.py:596 - step 3856: {'train/loss': 0.018657833337783813, 'train/grad_norm': 0.3251661956310272, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:09:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:09:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:16] train_sft.py:596 - step 3857: {'train/loss': 0.010063841938972473, 'train/grad_norm': 0.36100324988365173, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 17:09:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:09:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:20] train_sft.py:596 - step 3858: {'train/loss': 0.005984343588352203, 'train/grad_norm': 0.2717680037021637, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:09:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:09:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:25] train_sft.py:596 - step 3859: {'train/loss': 0.007365763187408447, 'train/grad_norm': 0.3946545124053955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:09:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:09:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:30] train_sft.py:596 - step 3860: {'train/loss': 0.006375625729560852, 'train/grad_norm': 0.30252954363822937, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 17:09:30] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:09:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:35] train_sft.py:596 - step 3861: {'train/loss': 0.013217227533459663, 'train/grad_norm': 0.3477519154548645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:09:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:09:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:40] train_sft.py:596 - step 3862: {'train/loss': 0.007916480302810669, 'train/grad_norm': 0.2866872251033783, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:09:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:09:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:45] train_sft.py:596 - step 3863: {'train/loss': 0.007855281233787537, 'train/grad_norm': 0.35720375180244446, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:09:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:09:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:49] train_sft.py:596 - step 3864: {'train/loss': 0.014720968902111053, 'train/grad_norm': 0.46693792939186096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:09:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:09:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:54] train_sft.py:596 - step 3865: {'train/loss': 0.021588653326034546, 'train/grad_norm': 0.3849131166934967, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:09:54] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:09:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:09:59] train_sft.py:596 - step 3866: {'train/loss': 0.008757337927818298, 'train/grad_norm': 0.2345992624759674, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:09:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:09:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:03] train_sft.py:596 - step 3867: {'train/loss': 0.01731392741203308, 'train/grad_norm': 0.7632573843002319, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:10:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:10:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:09] train_sft.py:596 - step 3868: {'train/loss': 0.011239297688007355, 'train/grad_norm': 0.46824121475219727, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.30s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.30s/it]
[2025-12-29 17:10:09] timer.py:32 - Timer actor_train end (elapsed: 5.3s)
[2025-12-29 17:10:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:14] train_sft.py:596 - step 3869: {'train/loss': 0.008876994252204895, 'train/grad_norm': 0.18888652324676514, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 17:10:14] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:10:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:19] train_sft.py:596 - step 3870: {'train/loss': 0.012121140956878662, 'train/grad_norm': 0.3861830234527588, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:10:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:10:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:23] train_sft.py:596 - step 3871: {'train/loss': 0.011294811964035034, 'train/grad_norm': 0.39637628197669983, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:10:23] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:10:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:28] train_sft.py:596 - step 3872: {'train/loss': 0.008229151368141174, 'train/grad_norm': 0.2035416066646576, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 17:10:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:10:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:33] train_sft.py:596 - step 3873: {'train/loss': 0.007113724946975708, 'train/grad_norm': 0.2523975670337677, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:10:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:10:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:39] train_sft.py:596 - step 3874: {'train/loss': 0.004401698708534241, 'train/grad_norm': 0.2435990273952484, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.13s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.13s/it]
[2025-12-29 17:10:39] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 17:10:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:43] train_sft.py:596 - step 3875: {'train/loss': 0.008954033255577087, 'train/grad_norm': 0.4295777380466461, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:10:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:10:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:48] train_sft.py:596 - step 3876: {'train/loss': 0.009188622236251831, 'train/grad_norm': 0.3262588083744049, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:10:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:10:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:53] train_sft.py:596 - step 3877: {'train/loss': 0.016267510131001472, 'train/grad_norm': 0.7853593230247498, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:10:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:10:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:10:57] train_sft.py:596 - step 3878: {'train/loss': 0.007731616497039795, 'train/grad_norm': 0.32731691002845764, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:10:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:10:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:03] train_sft.py:596 - step 3879: {'train/loss': 0.010001759976148605, 'train/grad_norm': 0.26927489042282104, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 17:11:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:11:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:07] train_sft.py:596 - step 3880: {'train/loss': 0.009106233716011047, 'train/grad_norm': 0.5072327256202698, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:11:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:11:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:12] train_sft.py:596 - step 3881: {'train/loss': 0.012724839150905609, 'train/grad_norm': 0.3497871160507202, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:11:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:17] train_sft.py:596 - step 3882: {'train/loss': 0.015272356569766998, 'train/grad_norm': 0.3260282874107361, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:11:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:21] train_sft.py:596 - step 3883: {'train/loss': 0.012588798999786377, 'train/grad_norm': 0.3050893545150757, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:11:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:26] train_sft.py:596 - step 3884: {'train/loss': 0.012219546362757683, 'train/grad_norm': 0.2605937123298645, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:11:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:31] train_sft.py:596 - step 3885: {'train/loss': 0.009799394756555557, 'train/grad_norm': 0.40443840622901917, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:11:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:11:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:35] train_sft.py:596 - step 3886: {'train/loss': 0.011647924780845642, 'train/grad_norm': 0.36032822728157043, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:11:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:40] train_sft.py:596 - step 3887: {'train/loss': 0.010632157325744629, 'train/grad_norm': 0.4734860956668854, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:11:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:11:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:45] train_sft.py:596 - step 3888: {'train/loss': 0.007402095943689346, 'train/grad_norm': 0.4574757218360901, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:11:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:49] train_sft.py:596 - step 3889: {'train/loss': 0.020908191800117493, 'train/grad_norm': 0.4192335903644562, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:11:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:11:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:54] train_sft.py:596 - step 3890: {'train/loss': 0.010508798062801361, 'train/grad_norm': 0.5268856883049011, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 17:11:54] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 17:11:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:11:59] train_sft.py:596 - step 3891: {'train/loss': 0.007027279585599899, 'train/grad_norm': 0.29254695773124695, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 17:11:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:11:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:04] train_sft.py:596 - step 3892: {'train/loss': 0.016263209283351898, 'train/grad_norm': 0.41467341780662537, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:12:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:12:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:08] train_sft.py:596 - step 3893: {'train/loss': 0.023709498345851898, 'train/grad_norm': 0.6843544244766235, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:12:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:12:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:13] train_sft.py:596 - step 3894: {'train/loss': 0.012034513056278229, 'train/grad_norm': 0.4101158082485199, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:12:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:12:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:18] train_sft.py:596 - step 3895: {'train/loss': 0.01075655221939087, 'train/grad_norm': 0.4044268727302551, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:12:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:12:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:23] train_sft.py:596 - step 3896: {'train/loss': 0.009510066360235214, 'train/grad_norm': 0.4155423641204834, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:12:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:12:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:27] train_sft.py:596 - step 3897: {'train/loss': 0.004704460501670837, 'train/grad_norm': 0.2676295340061188, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:12:27] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:12:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:32] train_sft.py:596 - step 3898: {'train/loss': 0.010565496981143951, 'train/grad_norm': 0.3023518919944763, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:12:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:12:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:37] train_sft.py:596 - step 3899: {'train/loss': 0.012829713523387909, 'train/grad_norm': 0.6577151417732239, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:12:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:12:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:42] train_sft.py:596 - step 3900: {'train/loss': 0.008919786661863327, 'train/grad_norm': 0.28464409708976746, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:12:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:12:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:47] train_sft.py:596 - step 3901: {'train/loss': 0.005401479080319405, 'train/grad_norm': 0.19615672528743744, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 17:12:47] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:12:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:51] train_sft.py:596 - step 3902: {'train/loss': 0.00824752077460289, 'train/grad_norm': 0.194523423910141, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:12:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:12:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:12:56] train_sft.py:596 - step 3903: {'train/loss': 0.010040313005447388, 'train/grad_norm': 0.33742448687553406, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:12:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:12:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:01] train_sft.py:596 - step 3904: {'train/loss': 0.01940278895199299, 'train/grad_norm': 0.7529318928718567, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 17:13:01] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:13:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:06] train_sft.py:596 - step 3905: {'train/loss': 0.02117028459906578, 'train/grad_norm': 0.9798501133918762, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:13:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:13:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:10] train_sft.py:596 - step 3906: {'train/loss': 0.021165698766708374, 'train/grad_norm': 0.4268401861190796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:13:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:13:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:15] train_sft.py:596 - step 3907: {'train/loss': 0.009431932121515274, 'train/grad_norm': 0.3746492266654968, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:13:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:13:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:20] train_sft.py:596 - step 3908: {'train/loss': 0.02468489110469818, 'train/grad_norm': 0.5722302794456482, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:13:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:13:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:24] train_sft.py:596 - step 3909: {'train/loss': 0.008052781224250793, 'train/grad_norm': 0.25220516324043274, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:13:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:13:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:29] train_sft.py:596 - step 3910: {'train/loss': 0.016789734363555908, 'train/grad_norm': 0.31746426224708557, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:13:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:13:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:34] train_sft.py:596 - step 3911: {'train/loss': 0.010046272538602352, 'train/grad_norm': 0.5633456707000732, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:13:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:13:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:39] train_sft.py:596 - step 3912: {'train/loss': 0.011883445084095001, 'train/grad_norm': 0.4222777783870697, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:13:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:13:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:43] train_sft.py:596 - step 3913: {'train/loss': 0.008775264024734497, 'train/grad_norm': 0.2625493109226227, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:13:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:13:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:48] train_sft.py:596 - step 3914: {'train/loss': 0.007025241851806641, 'train/grad_norm': 0.24416646361351013, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 17:13:48] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:13:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:52] train_sft.py:596 - step 3915: {'train/loss': 0.015191733837127686, 'train/grad_norm': 0.3582158386707306, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:13:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:13:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:13:57] train_sft.py:596 - step 3916: {'train/loss': 0.0063974373042583466, 'train/grad_norm': 0.35066139698028564, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 17:13:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:13:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:02] train_sft.py:596 - step 3917: {'train/loss': 0.01310612028464675, 'train/grad_norm': 0.4616945683956146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:14:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:14:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:07] train_sft.py:596 - step 3918: {'train/loss': 0.013058889657258987, 'train/grad_norm': 0.32711783051490784, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:14:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:14:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:11] train_sft.py:596 - step 3919: {'train/loss': 0.012947563081979752, 'train/grad_norm': 0.5894702672958374, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
[2025-12-29 17:14:11] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 17:14:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:16] train_sft.py:596 - step 3920: {'train/loss': 0.011496609076857567, 'train/grad_norm': 0.600972592830658, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 17:14:16] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:14:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:21] train_sft.py:596 - step 3921: {'train/loss': 0.005839012563228607, 'train/grad_norm': 0.21350489556789398, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 17:14:21] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:14:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:26] train_sft.py:596 - step 3922: {'train/loss': 0.011642858386039734, 'train/grad_norm': 0.2775574326515198, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:14:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:14:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:30] train_sft.py:596 - step 3923: {'train/loss': 0.012134864926338196, 'train/grad_norm': 0.33504095673561096, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:14:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:14:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:35] train_sft.py:596 - step 3924: {'train/loss': 0.013139712624251842, 'train/grad_norm': 0.4097574055194855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:14:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:14:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:40] train_sft.py:596 - step 3925: {'train/loss': 0.011081758886575699, 'train/grad_norm': 0.4931333065032959, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:14:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:14:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:45] train_sft.py:596 - step 3926: {'train/loss': 0.016714513301849365, 'train/grad_norm': 0.39151236414909363, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:14:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:14:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:49] train_sft.py:596 - step 3927: {'train/loss': 0.010486811399459839, 'train/grad_norm': 0.2780773639678955, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:14:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:14:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:54] train_sft.py:596 - step 3928: {'train/loss': 0.004552774131298065, 'train/grad_norm': 0.14973260462284088, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 17:14:54] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:14:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:14:59] train_sft.py:596 - step 3929: {'train/loss': 0.013591594994068146, 'train/grad_norm': 0.28916266560554504, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:14:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:14:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:04] train_sft.py:596 - step 3930: {'train/loss': 0.011529102921485901, 'train/grad_norm': 0.2516250014305115, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:15:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:15:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:08] train_sft.py:596 - step 3931: {'train/loss': 0.01074172556400299, 'train/grad_norm': 0.29892683029174805, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 17:15:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:15:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:13] train_sft.py:596 - step 3932: {'train/loss': 0.014903821051120758, 'train/grad_norm': 0.4850057363510132, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:15:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:15:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:18] train_sft.py:596 - step 3933: {'train/loss': 0.009834706783294678, 'train/grad_norm': 0.3489130139350891, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:15:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:15:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:23] train_sft.py:596 - step 3934: {'train/loss': 0.013436682522296906, 'train/grad_norm': 0.347554087638855, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:15:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:15:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:27] train_sft.py:596 - step 3935: {'train/loss': 0.009539119899272919, 'train/grad_norm': 0.33583787083625793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:15:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:15:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:32] train_sft.py:596 - step 3936: {'train/loss': 0.009913042187690735, 'train/grad_norm': 1.851385235786438, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:15:32] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:15:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:37] train_sft.py:596 - step 3937: {'train/loss': 0.014162898063659668, 'train/grad_norm': 0.5995565056800842, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:15:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:15:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:42] train_sft.py:596 - step 3938: {'train/loss': 0.006026141345500946, 'train/grad_norm': 0.3403562307357788, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:15:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:15:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:47] train_sft.py:596 - step 3939: {'train/loss': 0.007626712322235107, 'train/grad_norm': 0.24086517095565796, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 17:15:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:15:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:51] train_sft.py:596 - step 3940: {'train/loss': 0.008030295372009277, 'train/grad_norm': 0.3045744299888611, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:15:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:15:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:15:56] train_sft.py:596 - step 3941: {'train/loss': 0.011912673711776733, 'train/grad_norm': 0.40762782096862793, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:15:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:15:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:01] train_sft.py:596 - step 3942: {'train/loss': 0.01004725694656372, 'train/grad_norm': 0.3327098488807678, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:16:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:16:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:05] train_sft.py:596 - step 3943: {'train/loss': 0.012675344944000244, 'train/grad_norm': 0.48255881667137146, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:16:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:16:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:10] train_sft.py:596 - step 3944: {'train/loss': 0.012985683977603912, 'train/grad_norm': 0.49681785702705383, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:16:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:16:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:15] train_sft.py:596 - step 3945: {'train/loss': 0.016388650983572006, 'train/grad_norm': 0.6009426712989807, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:16:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:16:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:20] train_sft.py:596 - step 3946: {'train/loss': 0.01673451066017151, 'train/grad_norm': 0.41816845536231995, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 17:16:20] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:16:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:24] train_sft.py:596 - step 3947: {'train/loss': 0.016906216740608215, 'train/grad_norm': 0.38925036787986755, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:16:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:16:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:29] train_sft.py:596 - step 3948: {'train/loss': 0.008657827973365784, 'train/grad_norm': 0.23236793279647827, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:16:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:16:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:34] train_sft.py:596 - step 3949: {'train/loss': 0.016917848959565163, 'train/grad_norm': 0.4800793528556824, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 17:16:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:16:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:39] train_sft.py:596 - step 3950: {'train/loss': 0.013560958206653595, 'train/grad_norm': 0.7275592684745789, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 17:16:39] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:16:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:43] train_sft.py:596 - step 3951: {'train/loss': 0.011981584131717682, 'train/grad_norm': 0.3985459804534912, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:16:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:16:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:48] train_sft.py:596 - step 3952: {'train/loss': 0.013651259243488312, 'train/grad_norm': 0.3057100772857666, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:16:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:16:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:53] train_sft.py:596 - step 3953: {'train/loss': 0.007825382053852081, 'train/grad_norm': 0.277978777885437, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 17:16:53] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:16:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:16:58] train_sft.py:596 - step 3954: {'train/loss': 0.009854745119810104, 'train/grad_norm': 0.3920590281486511, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:16:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:16:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:03] train_sft.py:596 - step 3955: {'train/loss': 0.024820491671562195, 'train/grad_norm': 0.49495333433151245, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 17:17:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:17:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:08] train_sft.py:596 - step 3956: {'train/loss': 0.011841841042041779, 'train/grad_norm': 0.9960030317306519, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:17:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:17:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:13] train_sft.py:596 - step 3957: {'train/loss': 0.016635961830615997, 'train/grad_norm': 0.47854942083358765, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:17:13] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:17:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:18] train_sft.py:596 - step 3958: {'train/loss': 0.01012854278087616, 'train/grad_norm': 0.5352448225021362, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:17:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:17:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:23] train_sft.py:596 - step 3959: {'train/loss': 0.012588255107402802, 'train/grad_norm': 0.3573128283023834, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:17:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:17:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:27] train_sft.py:596 - step 3960: {'train/loss': 0.022328555583953857, 'train/grad_norm': 0.727338969707489, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:17:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:17:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:32] train_sft.py:596 - step 3961: {'train/loss': 0.014798677060753107, 'train/grad_norm': 0.8531075119972229, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:17:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:17:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:37] train_sft.py:596 - step 3962: {'train/loss': 0.01358293741941452, 'train/grad_norm': 0.6054041981697083, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 17:17:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:17:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:42] train_sft.py:596 - step 3963: {'train/loss': 0.015472590923309326, 'train/grad_norm': 0.5201581716537476, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 17:17:42] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:17:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:47] train_sft.py:596 - step 3964: {'train/loss': 0.016602888703346252, 'train/grad_norm': 0.8077306747436523, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 17:17:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:17:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:51] train_sft.py:596 - step 3965: {'train/loss': 0.011390089988708496, 'train/grad_norm': 0.45923927426338196, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:17:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:17:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:17:56] train_sft.py:596 - step 3966: {'train/loss': 0.025337345898151398, 'train/grad_norm': 0.6502984166145325, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:17:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:17:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:01] train_sft.py:596 - step 3967: {'train/loss': 0.01023823767900467, 'train/grad_norm': 0.36696720123291016, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 17:18:01] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:18:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:05] train_sft.py:596 - step 3968: {'train/loss': 0.01113002747297287, 'train/grad_norm': 0.3132571876049042, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:18:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:18:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:10] train_sft.py:596 - step 3969: {'train/loss': 0.017540186643600464, 'train/grad_norm': 0.38707220554351807, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:18:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:18:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:15] train_sft.py:596 - step 3970: {'train/loss': 0.013228118419647217, 'train/grad_norm': 0.38237491250038147, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:18:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:18:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:20] train_sft.py:596 - step 3971: {'train/loss': 0.00665321946144104, 'train/grad_norm': 0.21139192581176758, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:18:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:18:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:24] train_sft.py:596 - step 3972: {'train/loss': 0.02036299556493759, 'train/grad_norm': 0.736169159412384, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:18:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:18:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:29] train_sft.py:596 - step 3973: {'train/loss': 0.013845771551132202, 'train/grad_norm': 0.2906472682952881, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:18:29] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:18:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:34] train_sft.py:596 - step 3974: {'train/loss': 0.012007281184196472, 'train/grad_norm': 0.7850908041000366, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:18:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:18:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:39] train_sft.py:596 - step 3975: {'train/loss': 0.010588645935058594, 'train/grad_norm': 0.28870004415512085, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:18:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:18:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:43] train_sft.py:596 - step 3976: {'train/loss': 0.011230139061808586, 'train/grad_norm': 0.4430861473083496, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:18:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:18:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:48] train_sft.py:596 - step 3977: {'train/loss': 0.014576256275177002, 'train/grad_norm': 0.5094086527824402, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:18:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:18:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:52] train_sft.py:596 - step 3978: {'train/loss': 0.01729092001914978, 'train/grad_norm': 0.6579433083534241, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:18:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:18:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:18:57] train_sft.py:596 - step 3979: {'train/loss': 0.007332459092140198, 'train/grad_norm': 0.31522783637046814, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:18:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:18:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:03] train_sft.py:596 - step 3980: {'train/loss': 0.007907018065452576, 'train/grad_norm': 0.2774038016796112, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 17:19:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:19:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:07] train_sft.py:596 - step 3981: {'train/loss': 0.023010410368442535, 'train/grad_norm': 0.7882944941520691, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:19:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:19:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:12] train_sft.py:596 - step 3982: {'train/loss': 0.013382628560066223, 'train/grad_norm': 0.49397486448287964, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.07s/it]
[2025-12-29 17:19:12] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 17:19:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:17] train_sft.py:596 - step 3983: {'train/loss': 0.00799567624926567, 'train/grad_norm': 0.1740884631872177, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 17:19:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:19:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:22] train_sft.py:596 - step 3984: {'train/loss': 0.011367809027433395, 'train/grad_norm': 0.269996702671051, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:19:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:19:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:27] train_sft.py:596 - step 3985: {'train/loss': 0.007122676819562912, 'train/grad_norm': 0.3180885314941406, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:19:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:19:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:32] train_sft.py:596 - step 3986: {'train/loss': 0.013632744550704956, 'train/grad_norm': 0.4062028229236603, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:19:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:19:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:36] train_sft.py:596 - step 3987: {'train/loss': 0.013320446014404297, 'train/grad_norm': 0.7871565222740173, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:19:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:19:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:41] train_sft.py:596 - step 3988: {'train/loss': 0.015828900039196014, 'train/grad_norm': 0.718233048915863, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:19:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:19:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:46] train_sft.py:596 - step 3989: {'train/loss': 0.015461593866348267, 'train/grad_norm': 0.3737179636955261, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:19:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:19:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:50] train_sft.py:596 - step 3990: {'train/loss': 0.013605564832687378, 'train/grad_norm': 0.3253338932991028, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 17:19:50] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:19:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:19:55] train_sft.py:596 - step 3991: {'train/loss': 0.02096061408519745, 'train/grad_norm': 0.5280215740203857, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:19:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:19:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:00] train_sft.py:596 - step 3992: {'train/loss': 0.012873739004135132, 'train/grad_norm': 0.35262343287467957, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:20:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:20:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:04] train_sft.py:596 - step 3993: {'train/loss': 0.0076730018481612206, 'train/grad_norm': 0.38860777020454407, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 17:20:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:20:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:09] train_sft.py:596 - step 3994: {'train/loss': 0.014908984303474426, 'train/grad_norm': 0.3754555583000183, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:20:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:20:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:13] train_sft.py:596 - step 3995: {'train/loss': 0.016465209424495697, 'train/grad_norm': 0.4947022795677185, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:20:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:20:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:18] train_sft.py:596 - step 3996: {'train/loss': 0.01723414659500122, 'train/grad_norm': 0.5437730550765991, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 17:20:18] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:20:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:23] train_sft.py:596 - step 3997: {'train/loss': 0.012152265757322311, 'train/grad_norm': 0.5764195919036865, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:20:23] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:20:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:28] train_sft.py:596 - step 3998: {'train/loss': 0.028223183006048203, 'train/grad_norm': 1.0728254318237305, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:20:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:20:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:20:32] train_sft.py:596 - step 3999: {'train/loss': 0.020710229873657227, 'train/grad_norm': 0.6771464943885803, 'train/lr_0': 1e-05}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:20:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:20:44] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0004000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:02<04:17,  2.60s/it]
actor_val:   2%|▏         | 2/100 [00:04<03:19,  2.04s/it]
actor_val:   3%|▎         | 3/100 [00:05<02:55,  1.81s/it]
actor_val:   4%|▍         | 4/100 [00:07<02:44,  1.71s/it]
actor_val:   5%|▌         | 5/100 [00:08<02:37,  1.66s/it]
actor_val:   6%|▌         | 6/100 [00:10<02:32,  1.63s/it]
actor_val:   7%|▋         | 7/100 [00:12<02:28,  1.60s/it]
actor_val:   8%|▊         | 8/100 [00:13<02:25,  1.58s/it]
actor_val:   9%|▉         | 9/100 [00:15<02:22,  1.57s/it]
actor_val:  10%|█         | 10/100 [00:16<02:21,  1.57s/it]
actor_val:  11%|█         | 11/100 [00:18<02:18,  1.56s/it]
actor_val:  12%|█▏        | 12/100 [00:19<02:17,  1.56s/it]
actor_val:  13%|█▎        | 13/100 [00:21<02:21,  1.63s/it]
actor_val:  14%|█▍        | 14/100 [00:23<02:17,  1.60s/it]
actor_val:  15%|█▌        | 15/100 [00:24<02:15,  1.60s/it]
actor_val:  16%|█▌        | 16/100 [00:26<02:18,  1.65s/it]
actor_val:  17%|█▋        | 17/100 [00:28<02:19,  1.68s/it]
actor_val:  18%|█▊        | 18/100 [00:29<02:14,  1.64s/it]
actor_val:  19%|█▉        | 19/100 [00:31<02:10,  1.61s/it]
actor_val:  20%|██        | 20/100 [00:32<02:06,  1.58s/it]
actor_val:  21%|██        | 21/100 [00:34<02:04,  1.57s/it]
actor_val:  22%|██▏       | 22/100 [00:36<02:08,  1.64s/it]
actor_val:  23%|██▎       | 23/100 [00:37<02:05,  1.62s/it]
actor_val:  24%|██▍       | 24/100 [00:39<02:01,  1.60s/it]
actor_val:  25%|██▌       | 25/100 [00:40<01:59,  1.59s/it]
actor_val:  26%|██▌       | 26/100 [00:42<01:56,  1.58s/it]
actor_val:  27%|██▋       | 27/100 [00:43<01:54,  1.57s/it]
actor_val:  28%|██▊       | 28/100 [00:45<01:52,  1.56s/it]
actor_val:  29%|██▉       | 29/100 [00:47<01:50,  1.56s/it]
actor_val:  30%|███       | 30/100 [00:48<01:48,  1.54s/it]
actor_val:  31%|███       | 31/100 [00:50<01:52,  1.63s/it]
actor_val:  32%|███▏      | 32/100 [00:51<01:49,  1.60s/it]
actor_val:  33%|███▎      | 33/100 [00:53<01:46,  1.59s/it]
actor_val:  34%|███▍      | 34/100 [00:55<01:54,  1.73s/it]
actor_val:  35%|███▌      | 35/100 [00:57<01:49,  1.69s/it]
actor_val:  36%|███▌      | 36/100 [00:58<01:45,  1.65s/it]
actor_val:  37%|███▋      | 37/100 [01:00<01:42,  1.62s/it]
actor_val:  38%|███▊      | 38/100 [01:01<01:38,  1.58s/it]
actor_val:  39%|███▉      | 39/100 [01:03<01:40,  1.64s/it]
actor_val:  40%|████      | 40/100 [01:05<01:37,  1.62s/it]
actor_val:  41%|████      | 41/100 [01:06<01:34,  1.61s/it]
actor_val:  42%|████▏     | 42/100 [01:08<01:32,  1.59s/it]
actor_val:  43%|████▎     | 43/100 [01:09<01:29,  1.57s/it]
actor_val:  44%|████▍     | 44/100 [01:11<01:27,  1.56s/it]
actor_val:  45%|████▌     | 45/100 [01:12<01:26,  1.57s/it]
actor_val:  46%|████▌     | 46/100 [01:14<01:23,  1.55s/it]
actor_val:  47%|████▋     | 47/100 [01:16<01:25,  1.62s/it]
actor_val:  48%|████▊     | 48/100 [01:17<01:22,  1.60s/it]
actor_val:  49%|████▉     | 49/100 [01:19<01:20,  1.57s/it]
actor_val:  50%|█████     | 50/100 [01:20<01:17,  1.56s/it]
actor_val:  51%|█████     | 51/100 [01:22<01:15,  1.54s/it]
actor_val:  52%|█████▏    | 52/100 [01:24<01:17,  1.62s/it]
actor_val:  53%|█████▎    | 53/100 [01:25<01:15,  1.60s/it]
actor_val:  54%|█████▍    | 54/100 [01:27<01:12,  1.58s/it]
actor_val:  55%|█████▌    | 55/100 [01:28<01:11,  1.58s/it]
actor_val:  56%|█████▌    | 56/100 [01:30<01:12,  1.66s/it]
actor_val:  57%|█████▋    | 57/100 [01:32<01:09,  1.62s/it]
actor_val:  58%|█████▊    | 58/100 [01:33<01:07,  1.60s/it]
actor_val:  59%|█████▉    | 59/100 [01:35<01:04,  1.58s/it]
actor_val:  60%|██████    | 60/100 [01:36<01:05,  1.65s/it]
actor_val:  61%|██████    | 61/100 [01:38<01:05,  1.69s/it]
actor_val:  62%|██████▏   | 62/100 [01:40<01:02,  1.64s/it]
actor_val:  63%|██████▎   | 63/100 [01:41<00:59,  1.62s/it]
actor_val:  64%|██████▍   | 64/100 [01:43<00:57,  1.61s/it]
actor_val:  65%|██████▌   | 65/100 [01:44<00:55,  1.58s/it]
actor_val:  66%|██████▌   | 66/100 [01:46<00:56,  1.65s/it]
actor_val:  67%|██████▋   | 67/100 [01:48<00:56,  1.71s/it]
actor_val:  68%|██████▊   | 68/100 [01:50<00:52,  1.66s/it]
actor_val:  69%|██████▉   | 69/100 [01:51<00:50,  1.63s/it]
actor_val:  70%|███████   | 70/100 [01:53<00:47,  1.60s/it]
actor_val:  71%|███████   | 71/100 [01:54<00:45,  1.58s/it]
actor_val:  72%|███████▏  | 72/100 [01:56<00:44,  1.57s/it]
actor_val:  73%|███████▎  | 73/100 [01:58<00:44,  1.63s/it]
actor_val:  74%|███████▍  | 74/100 [01:59<00:44,  1.70s/it]
actor_val:  75%|███████▌  | 75/100 [02:01<00:41,  1.66s/it]
actor_val:  76%|███████▌  | 76/100 [02:03<00:38,  1.62s/it]
actor_val:  77%|███████▋  | 77/100 [02:04<00:36,  1.59s/it]
actor_val:  78%|███████▊  | 78/100 [02:06<00:36,  1.65s/it]
actor_val:  79%|███████▉  | 79/100 [02:08<00:35,  1.69s/it]
actor_val:  80%|████████  | 80/100 [02:09<00:32,  1.64s/it]
actor_val:  81%|████████  | 81/100 [02:11<00:30,  1.62s/it]
actor_val:  82%|████████▏ | 82/100 [02:12<00:28,  1.60s/it]
actor_val:  83%|████████▎ | 83/100 [02:14<00:28,  1.66s/it]
actor_val:  84%|████████▍ | 84/100 [02:16<00:27,  1.70s/it]
actor_val:  85%|████████▌ | 85/100 [02:17<00:24,  1.65s/it]
actor_val:  86%|████████▌ | 86/100 [02:19<00:22,  1.62s/it]
actor_val:  87%|████████▋ | 87/100 [02:21<00:21,  1.67s/it]
actor_val:  88%|████████▊ | 88/100 [02:22<00:19,  1.63s/it]
actor_val:  89%|████████▉ | 89/100 [02:24<00:18,  1.68s/it]
actor_val:  90%|█████████ | 90/100 [02:26<00:17,  1.71s/it]
actor_val:  91%|█████████ | 91/100 [02:27<00:14,  1.66s/it]
actor_val:  92%|█████████▏| 92/100 [02:29<00:13,  1.70s/it]
actor_val:  93%|█████████▎| 93/100 [02:31<00:11,  1.66s/it]
actor_val:  94%|█████████▍| 94/100 [02:32<00:09,  1.63s/it]
actor_val:  95%|█████████▌| 95/100 [02:34<00:07,  1.60s/it]
actor_val:  96%|█████████▌| 96/100 [02:35<00:06,  1.57s/it]
actor_val:  97%|█████████▋| 97/100 [02:37<00:04,  1.56s/it]
actor_val:  98%|█████████▊| 98/100 [02:39<00:03,  1.63s/it]
actor_val:  99%|█████████▉| 99/100 [02:40<00:01,  1.60s/it]
actor_val: 100%|██████████| 100/100 [02:42<00:00,  1.58s/it]
actor_val: 100%|██████████| 100/100 [02:42<00:00,  1.62s/it]
[2025-12-29 17:23:27] train_sft.py:658 - step 4000: {'val/loss': 0.48328125}
[2025-12-29 17:23:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:23:32] train_sft.py:596 - step 4000: {'train/loss': 0.011007796972990036, 'train/grad_norm': 0.3151859939098358, 'train/lr_0': 9.990000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 17:23:32] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:23:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:23:37] train_sft.py:596 - step 4001: {'train/loss': 0.015562191605567932, 'train/grad_norm': 0.47848084568977356, 'train/lr_0': 9.980000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:23:37] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:23:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:23:42] train_sft.py:596 - step 4002: {'train/loss': 0.012106142938137054, 'train/grad_norm': 0.3955579698085785, 'train/lr_0': 9.970000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:23:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:23:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:23:46] train_sft.py:596 - step 4003: {'train/loss': 0.008794546127319336, 'train/grad_norm': 0.2699224054813385, 'train/lr_0': 9.960000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:23:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:23:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:23:51] train_sft.py:596 - step 4004: {'train/loss': 0.009975485503673553, 'train/grad_norm': 0.2402833253145218, 'train/lr_0': 9.950000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 17:23:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:23:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:23:55] train_sft.py:596 - step 4005: {'train/loss': 0.01748593896627426, 'train/grad_norm': 0.4167361855506897, 'train/lr_0': 9.940000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:23:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:23:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:00] train_sft.py:596 - step 4006: {'train/loss': 0.012669350951910019, 'train/grad_norm': 0.46062034368515015, 'train/lr_0': 9.930000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:24:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:24:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:05] train_sft.py:596 - step 4007: {'train/loss': 0.010515332221984863, 'train/grad_norm': 0.29839277267456055, 'train/lr_0': 9.920000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:24:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:24:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:09] train_sft.py:596 - step 4008: {'train/loss': 0.014019355177879333, 'train/grad_norm': 0.48505935072898865, 'train/lr_0': 9.91e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:24:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:24:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:14] train_sft.py:596 - step 4009: {'train/loss': 0.008186683058738708, 'train/grad_norm': 0.4725315570831299, 'train/lr_0': 9.9e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:24:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:24:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:18] train_sft.py:596 - step 4010: {'train/loss': 0.014271408319473267, 'train/grad_norm': 0.557998538017273, 'train/lr_0': 9.89e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:24:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:24:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:23] train_sft.py:596 - step 4011: {'train/loss': 0.013378627598285675, 'train/grad_norm': 0.3123086392879486, 'train/lr_0': 9.88e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:24:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:24:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:28] train_sft.py:596 - step 4012: {'train/loss': 0.01352497935295105, 'train/grad_norm': 0.3535277545452118, 'train/lr_0': 9.87e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 17:24:28] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:24:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:33] train_sft.py:596 - step 4013: {'train/loss': 0.010449469089508057, 'train/grad_norm': 0.3691154718399048, 'train/lr_0': 9.86e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:24:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:24:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:37] train_sft.py:596 - step 4014: {'train/loss': 0.01284019649028778, 'train/grad_norm': 0.702167272567749, 'train/lr_0': 9.85e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:24:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:24:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:43] train_sft.py:596 - step 4015: {'train/loss': 0.008467279374599457, 'train/grad_norm': 0.25900521874427795, 'train/lr_0': 9.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 17:24:43] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:24:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:48] train_sft.py:596 - step 4016: {'train/loss': 0.010192908346652985, 'train/grad_norm': 0.43829792737960815, 'train/lr_0': 9.83e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:24:48] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:24:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:52] train_sft.py:596 - step 4017: {'train/loss': 0.02084965631365776, 'train/grad_norm': 0.6174266934394836, 'train/lr_0': 9.820000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:24:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:24:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:24:57] train_sft.py:596 - step 4018: {'train/loss': 0.014994017779827118, 'train/grad_norm': 0.2359846532344818, 'train/lr_0': 9.810000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:24:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:24:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:01] train_sft.py:596 - step 4019: {'train/loss': 0.015289798378944397, 'train/grad_norm': 0.5909190773963928, 'train/lr_0': 9.800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:25:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:25:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:06] train_sft.py:596 - step 4020: {'train/loss': 0.006680198013782501, 'train/grad_norm': 0.17439351975917816, 'train/lr_0': 9.790000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:25:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:25:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:11] train_sft.py:596 - step 4021: {'train/loss': 0.01773127820342779, 'train/grad_norm': 0.7951918244361877, 'train/lr_0': 9.780000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:25:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:25:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:15] train_sft.py:596 - step 4022: {'train/loss': 0.019016757607460022, 'train/grad_norm': 0.6200341582298279, 'train/lr_0': 9.770000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:25:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:25:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:20] train_sft.py:596 - step 4023: {'train/loss': 0.022367477416992188, 'train/grad_norm': 0.6207124590873718, 'train/lr_0': 9.760000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:25:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:25:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:25] train_sft.py:596 - step 4024: {'train/loss': 0.016632400453090668, 'train/grad_norm': 0.3213675618171692, 'train/lr_0': 9.75e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 17:25:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:25:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:30] train_sft.py:596 - step 4025: {'train/loss': 0.01606491208076477, 'train/grad_norm': 0.8373103141784668, 'train/lr_0': 9.74e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:25:30] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:25:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:35] train_sft.py:596 - step 4026: {'train/loss': 0.011859163641929626, 'train/grad_norm': 0.2947479784488678, 'train/lr_0': 9.73e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:25:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:25:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:40] train_sft.py:596 - step 4027: {'train/loss': 0.015346229076385498, 'train/grad_norm': 0.3026053011417389, 'train/lr_0': 9.72e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:25:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:25:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:44] train_sft.py:596 - step 4028: {'train/loss': 0.01611478626728058, 'train/grad_norm': 0.8034793138504028, 'train/lr_0': 9.71e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:25:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:25:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:49] train_sft.py:596 - step 4029: {'train/loss': 0.010678194463253021, 'train/grad_norm': 0.24787884950637817, 'train/lr_0': 9.7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:25:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:25:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:54] train_sft.py:596 - step 4030: {'train/loss': 0.01231054961681366, 'train/grad_norm': 0.5507547855377197, 'train/lr_0': 9.69e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 17:25:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:25:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:25:59] train_sft.py:596 - step 4031: {'train/loss': 0.013174831867218018, 'train/grad_norm': 0.48015037178993225, 'train/lr_0': 9.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:25:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:25:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:04] train_sft.py:596 - step 4032: {'train/loss': 0.017435193061828613, 'train/grad_norm': 0.5648930072784424, 'train/lr_0': 9.67e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:26:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:26:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:08] train_sft.py:596 - step 4033: {'train/loss': 0.006500720977783203, 'train/grad_norm': 0.4008605480194092, 'train/lr_0': 9.66e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:26:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:26:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:13] train_sft.py:596 - step 4034: {'train/loss': 0.014250561594963074, 'train/grad_norm': 0.43404605984687805, 'train/lr_0': 9.65e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:26:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:26:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:18] train_sft.py:596 - step 4035: {'train/loss': 0.011016875505447388, 'train/grad_norm': 0.32500436902046204, 'train/lr_0': 9.640000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:26:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:26:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:23] train_sft.py:596 - step 4036: {'train/loss': 0.008686736226081848, 'train/grad_norm': 0.3225369453430176, 'train/lr_0': 9.630000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:26:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:26:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:27] train_sft.py:596 - step 4037: {'train/loss': 0.01464293897151947, 'train/grad_norm': 0.3611072301864624, 'train/lr_0': 9.620000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:26:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:26:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:32] train_sft.py:596 - step 4038: {'train/loss': 0.010934233665466309, 'train/grad_norm': 0.33618173003196716, 'train/lr_0': 9.610000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:26:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:26:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:36] train_sft.py:596 - step 4039: {'train/loss': 0.012083657085895538, 'train/grad_norm': 0.33067914843559265, 'train/lr_0': 9.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:26:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:26:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:42] train_sft.py:596 - step 4040: {'train/loss': 0.01709301769733429, 'train/grad_norm': 0.5142472386360168, 'train/lr_0': 9.59e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:26:42] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:26:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:46] train_sft.py:596 - step 4041: {'train/loss': 0.010238513350486755, 'train/grad_norm': 0.4921795427799225, 'train/lr_0': 9.58e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:26:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:26:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:51] train_sft.py:596 - step 4042: {'train/loss': 0.01068788766860962, 'train/grad_norm': 0.44373732805252075, 'train/lr_0': 9.57e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:26:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:26:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:26:56] train_sft.py:596 - step 4043: {'train/loss': 0.010687842965126038, 'train/grad_norm': 0.3163923919200897, 'train/lr_0': 9.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:26:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:26:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:01] train_sft.py:596 - step 4044: {'train/loss': 0.013134805485606194, 'train/grad_norm': 0.32216453552246094, 'train/lr_0': 9.55e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:27:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:27:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:05] train_sft.py:596 - step 4045: {'train/loss': 0.017520368099212646, 'train/grad_norm': 1.5006663799285889, 'train/lr_0': 9.54e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:27:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:27:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:10] train_sft.py:596 - step 4046: {'train/loss': 0.009468287229537964, 'train/grad_norm': 0.1376696228981018, 'train/lr_0': 9.53e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
[2025-12-29 17:27:10] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 17:27:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:14] train_sft.py:596 - step 4047: {'train/loss': 0.018772438168525696, 'train/grad_norm': 0.5661402344703674, 'train/lr_0': 9.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:27:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:27:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:19] train_sft.py:596 - step 4048: {'train/loss': 0.013813287019729614, 'train/grad_norm': 0.4034070670604706, 'train/lr_0': 9.51e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:27:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:27:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:24] train_sft.py:596 - step 4049: {'train/loss': 0.015941813588142395, 'train/grad_norm': 0.5410232543945312, 'train/lr_0': 9.5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:27:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:27:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:29] train_sft.py:596 - step 4050: {'train/loss': 0.01819860190153122, 'train/grad_norm': 0.5503418445587158, 'train/lr_0': 9.49e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:27:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:27:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:34] train_sft.py:596 - step 4051: {'train/loss': 0.02052117884159088, 'train/grad_norm': 0.7471445202827454, 'train/lr_0': 9.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 17:27:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:27:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:39] train_sft.py:596 - step 4052: {'train/loss': 0.013873428106307983, 'train/grad_norm': 0.5664147734642029, 'train/lr_0': 9.47e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:27:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:27:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:43] train_sft.py:596 - step 4053: {'train/loss': 0.013330847024917603, 'train/grad_norm': 0.583366870880127, 'train/lr_0': 9.460000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:27:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:27:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:48] train_sft.py:596 - step 4054: {'train/loss': 0.007861025631427765, 'train/grad_norm': 0.38823404908180237, 'train/lr_0': 9.450000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:27:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:27:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:53] train_sft.py:596 - step 4055: {'train/loss': 0.008507303893566132, 'train/grad_norm': 0.29776081442832947, 'train/lr_0': 9.440000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 17:27:53] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:27:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:27:57] train_sft.py:596 - step 4056: {'train/loss': 0.007568277418613434, 'train/grad_norm': 0.3648703992366791, 'train/lr_0': 9.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:27:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:27:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:02] train_sft.py:596 - step 4057: {'train/loss': 0.010696619749069214, 'train/grad_norm': 0.49870187044143677, 'train/lr_0': 9.42e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:28:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:28:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:07] train_sft.py:596 - step 4058: {'train/loss': 0.0032245516777038574, 'train/grad_norm': 0.26281532645225525, 'train/lr_0': 9.410000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:28:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:28:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:11] train_sft.py:596 - step 4059: {'train/loss': 0.012503907084465027, 'train/grad_norm': 0.515904426574707, 'train/lr_0': 9.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:28:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:28:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:16] train_sft.py:596 - step 4060: {'train/loss': 0.005541384220123291, 'train/grad_norm': 0.33389899134635925, 'train/lr_0': 9.390000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:28:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:28:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:20] train_sft.py:596 - step 4061: {'train/loss': 0.007937073707580566, 'train/grad_norm': 0.16468308866024017, 'train/lr_0': 9.38e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:28:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:28:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:25] train_sft.py:596 - step 4062: {'train/loss': 0.013685926795005798, 'train/grad_norm': 0.5383875370025635, 'train/lr_0': 9.370000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:28:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:28:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:30] train_sft.py:596 - step 4063: {'train/loss': 0.012729473412036896, 'train/grad_norm': 0.39684709906578064, 'train/lr_0': 9.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 17:28:30] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:28:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:35] train_sft.py:596 - step 4064: {'train/loss': 0.008474975824356079, 'train/grad_norm': 0.2905811369419098, 'train/lr_0': 9.350000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:28:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:28:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:40] train_sft.py:596 - step 4065: {'train/loss': 0.005173996090888977, 'train/grad_norm': 0.23229816555976868, 'train/lr_0': 9.34e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:28:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:28:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:44] train_sft.py:596 - step 4066: {'train/loss': 0.008050546050071716, 'train/grad_norm': 0.2405940145254135, 'train/lr_0': 9.33e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:28:44] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:28:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:49] train_sft.py:596 - step 4067: {'train/loss': 0.007174648344516754, 'train/grad_norm': 0.37435540556907654, 'train/lr_0': 9.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:28:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:28:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:54] train_sft.py:596 - step 4068: {'train/loss': 0.011584077030420303, 'train/grad_norm': 0.573945939540863, 'train/lr_0': 9.31e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:28:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:28:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:28:59] train_sft.py:596 - step 4069: {'train/loss': 0.01587018370628357, 'train/grad_norm': 0.5889546275138855, 'train/lr_0': 9.3e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:28:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:28:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:03] train_sft.py:596 - step 4070: {'train/loss': 0.012540414929389954, 'train/grad_norm': 0.8344781398773193, 'train/lr_0': 9.29e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:29:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:29:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:08] train_sft.py:596 - step 4071: {'train/loss': 0.015106387436389923, 'train/grad_norm': 0.3238750696182251, 'train/lr_0': 9.280000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:29:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:29:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:13] train_sft.py:596 - step 4072: {'train/loss': 0.0056758299469947815, 'train/grad_norm': 0.2843720614910126, 'train/lr_0': 9.270000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 17:29:13] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:29:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:17] train_sft.py:596 - step 4073: {'train/loss': 0.0072260163724422455, 'train/grad_norm': 0.3476901650428772, 'train/lr_0': 9.260000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:29:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:29:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:22] train_sft.py:596 - step 4074: {'train/loss': 0.012395196594297886, 'train/grad_norm': 0.3432048261165619, 'train/lr_0': 9.250000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:29:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:29:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:27] train_sft.py:596 - step 4075: {'train/loss': 0.00986132025718689, 'train/grad_norm': 0.35607674717903137, 'train/lr_0': 9.240000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:29:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:29:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:31] train_sft.py:596 - step 4076: {'train/loss': 0.008643150329589844, 'train/grad_norm': 0.2849080264568329, 'train/lr_0': 9.230000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:29:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:29:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:36] train_sft.py:596 - step 4077: {'train/loss': 0.007454797625541687, 'train/grad_norm': 0.3054088056087494, 'train/lr_0': 9.220000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:29:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:29:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:41] train_sft.py:596 - step 4078: {'train/loss': 0.003364652395248413, 'train/grad_norm': 0.21174412965774536, 'train/lr_0': 9.210000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 17:29:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:29:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:45] train_sft.py:596 - step 4079: {'train/loss': 0.007021445780992508, 'train/grad_norm': 0.30613473057746887, 'train/lr_0': 9.200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:29:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:29:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:50] train_sft.py:596 - step 4080: {'train/loss': 0.008171513676643372, 'train/grad_norm': 0.3141651153564453, 'train/lr_0': 9.190000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:29:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:29:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:54] train_sft.py:596 - step 4081: {'train/loss': 0.005842534825205803, 'train/grad_norm': 0.24971918761730194, 'train/lr_0': 9.180000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 17:29:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:29:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:29:59] train_sft.py:596 - step 4082: {'train/loss': 0.009534820914268494, 'train/grad_norm': 0.27657684683799744, 'train/lr_0': 9.17e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:29:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:29:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:04] train_sft.py:596 - step 4083: {'train/loss': 0.013178884983062744, 'train/grad_norm': 0.47064539790153503, 'train/lr_0': 9.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 17:30:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:30:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:09] train_sft.py:596 - step 4084: {'train/loss': 0.006974425166845322, 'train/grad_norm': 0.23097558319568634, 'train/lr_0': 9.15e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:30:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:30:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:14] train_sft.py:596 - step 4085: {'train/loss': 0.010939549654722214, 'train/grad_norm': 0.38556745648384094, 'train/lr_0': 9.14e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:30:14] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:30:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:19] train_sft.py:596 - step 4086: {'train/loss': 0.018420279026031494, 'train/grad_norm': 0.5419118404388428, 'train/lr_0': 9.13e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:30:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:30:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:23] train_sft.py:596 - step 4087: {'train/loss': 0.011884897947311401, 'train/grad_norm': 0.2631562352180481, 'train/lr_0': 9.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:30:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:30:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:28] train_sft.py:596 - step 4088: {'train/loss': 0.011208273470401764, 'train/grad_norm': 0.4430570602416992, 'train/lr_0': 9.110000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:30:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:30:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:33] train_sft.py:596 - step 4089: {'train/loss': 0.007189624011516571, 'train/grad_norm': 0.24519270658493042, 'train/lr_0': 9.100000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:30:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:30:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:37] train_sft.py:596 - step 4090: {'train/loss': 0.01067018136382103, 'train/grad_norm': 0.4599483609199524, 'train/lr_0': 9.090000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:30:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:30:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:42] train_sft.py:596 - step 4091: {'train/loss': 0.011122971773147583, 'train/grad_norm': 0.3094140887260437, 'train/lr_0': 9.080000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:30:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:30:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:47] train_sft.py:596 - step 4092: {'train/loss': 0.01049753651022911, 'train/grad_norm': 0.3741466701030731, 'train/lr_0': 9.070000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:30:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:30:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:51] train_sft.py:596 - step 4093: {'train/loss': 0.008743926882743835, 'train/grad_norm': 0.45178186893463135, 'train/lr_0': 9.060000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:30:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:30:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:30:56] train_sft.py:596 - step 4094: {'train/loss': 0.006996776908636093, 'train/grad_norm': 0.15978693962097168, 'train/lr_0': 9.050000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 17:30:56] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:30:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:01] train_sft.py:596 - step 4095: {'train/loss': 0.008734878152608871, 'train/grad_norm': 0.4545547664165497, 'train/lr_0': 9.040000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:31:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:31:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:06] train_sft.py:596 - step 4096: {'train/loss': 0.01256529986858368, 'train/grad_norm': 0.4112105965614319, 'train/lr_0': 9.030000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 17:31:06] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:31:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:10] train_sft.py:596 - step 4097: {'train/loss': 0.017592940479516983, 'train/grad_norm': 0.7644093036651611, 'train/lr_0': 9.020000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:31:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:31:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:15] train_sft.py:596 - step 4098: {'train/loss': 0.008650990203022957, 'train/grad_norm': 0.2803535461425781, 'train/lr_0': 9.01e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:31:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:31:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:20] train_sft.py:596 - step 4099: {'train/loss': 0.007359787821769714, 'train/grad_norm': 0.5438874959945679, 'train/lr_0': 9e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:31:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:31:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:25] train_sft.py:596 - step 4100: {'train/loss': 0.009482994675636292, 'train/grad_norm': 0.5438469648361206, 'train/lr_0': 8.99e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:31:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:31:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:29] train_sft.py:596 - step 4101: {'train/loss': 0.013077758252620697, 'train/grad_norm': 0.23578420281410217, 'train/lr_0': 8.98e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:31:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:31:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:34] train_sft.py:596 - step 4102: {'train/loss': 0.010763555765151978, 'train/grad_norm': 0.3879985511302948, 'train/lr_0': 8.97e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:31:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:31:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:39] train_sft.py:596 - step 4103: {'train/loss': 0.012853052467107773, 'train/grad_norm': 0.4762883484363556, 'train/lr_0': 8.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 17:31:39] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:31:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:44] train_sft.py:596 - step 4104: {'train/loss': 0.011984921991825104, 'train/grad_norm': 0.6500150561332703, 'train/lr_0': 8.95e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:31:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:31:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:48] train_sft.py:596 - step 4105: {'train/loss': 0.0076193660497665405, 'train/grad_norm': 0.36484190821647644, 'train/lr_0': 8.94e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:31:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:31:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:53] train_sft.py:596 - step 4106: {'train/loss': 0.0062648579478263855, 'train/grad_norm': 0.22982966899871826, 'train/lr_0': 8.930000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:31:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:31:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:31:58] train_sft.py:596 - step 4107: {'train/loss': 0.01092512160539627, 'train/grad_norm': 0.3839939832687378, 'train/lr_0': 8.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:31:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:31:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:02] train_sft.py:596 - step 4108: {'train/loss': 0.014954730868339539, 'train/grad_norm': 0.37096813321113586, 'train/lr_0': 8.910000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:32:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:32:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:07] train_sft.py:596 - step 4109: {'train/loss': 0.01277148351073265, 'train/grad_norm': 0.6289478540420532, 'train/lr_0': 8.900000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:32:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:32:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:12] train_sft.py:596 - step 4110: {'train/loss': 0.014319382607936859, 'train/grad_norm': 0.20108148455619812, 'train/lr_0': 8.890000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:32:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:32:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:16] train_sft.py:596 - step 4111: {'train/loss': 0.010806053876876831, 'train/grad_norm': 0.3315258026123047, 'train/lr_0': 8.880000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:32:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:32:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:21] train_sft.py:596 - step 4112: {'train/loss': 0.01847483217716217, 'train/grad_norm': 0.3746190071105957, 'train/lr_0': 8.870000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:32:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:32:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:26] train_sft.py:596 - step 4113: {'train/loss': 0.013876527547836304, 'train/grad_norm': 0.6320390701293945, 'train/lr_0': 8.860000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 17:32:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:32:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:30] train_sft.py:596 - step 4114: {'train/loss': 0.005470670759677887, 'train/grad_norm': 0.2516123056411743, 'train/lr_0': 8.85e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:32:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:32:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:35] train_sft.py:596 - step 4115: {'train/loss': 0.00875307247042656, 'train/grad_norm': 0.3389013111591339, 'train/lr_0': 8.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:32:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:32:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:40] train_sft.py:596 - step 4116: {'train/loss': 0.00581137090921402, 'train/grad_norm': 0.3925992548465729, 'train/lr_0': 8.83e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:32:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:32:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:45] train_sft.py:596 - step 4117: {'train/loss': 0.008983809500932693, 'train/grad_norm': 0.29412776231765747, 'train/lr_0': 8.82e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:32:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:32:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:49] train_sft.py:596 - step 4118: {'train/loss': 0.0065513961017131805, 'train/grad_norm': 0.3373350501060486, 'train/lr_0': 8.81e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:32:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:32:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:54] train_sft.py:596 - step 4119: {'train/loss': 0.02223743498325348, 'train/grad_norm': 0.4425044357776642, 'train/lr_0': 8.8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:32:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:32:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:32:59] train_sft.py:596 - step 4120: {'train/loss': 0.00851419847458601, 'train/grad_norm': 0.36821067333221436, 'train/lr_0': 8.79e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:32:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:32:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:04] train_sft.py:596 - step 4121: {'train/loss': 0.010424938052892685, 'train/grad_norm': 0.32040345668792725, 'train/lr_0': 8.78e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 17:33:04] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:33:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:09] train_sft.py:596 - step 4122: {'train/loss': 0.014243081212043762, 'train/grad_norm': 0.7756497263908386, 'train/lr_0': 8.77e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:33:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:33:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:14] train_sft.py:596 - step 4123: {'train/loss': 0.011386841535568237, 'train/grad_norm': 0.43567955493927, 'train/lr_0': 8.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 17:33:14] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:33:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:19] train_sft.py:596 - step 4124: {'train/loss': 0.009164627641439438, 'train/grad_norm': 0.2275351583957672, 'train/lr_0': 8.750000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 17:33:19] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:33:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:23] train_sft.py:596 - step 4125: {'train/loss': 0.0074096620082855225, 'train/grad_norm': 0.26189613342285156, 'train/lr_0': 8.740000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:33:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:33:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:28] train_sft.py:596 - step 4126: {'train/loss': 0.011234153062105179, 'train/grad_norm': 0.32836467027664185, 'train/lr_0': 8.730000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:33:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:33:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:33] train_sft.py:596 - step 4127: {'train/loss': 0.01324160024523735, 'train/grad_norm': 0.38907095789909363, 'train/lr_0': 8.720000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:33:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:33:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:38] train_sft.py:596 - step 4128: {'train/loss': 0.010540924966335297, 'train/grad_norm': 0.43564391136169434, 'train/lr_0': 8.710000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:33:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:33:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:43] train_sft.py:596 - step 4129: {'train/loss': 0.020745068788528442, 'train/grad_norm': 0.5669536590576172, 'train/lr_0': 8.700000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:33:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:33:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:47] train_sft.py:596 - step 4130: {'train/loss': 0.017084598541259766, 'train/grad_norm': 0.5037515163421631, 'train/lr_0': 8.690000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:33:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:33:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:52] train_sft.py:596 - step 4131: {'train/loss': 0.01492975652217865, 'train/grad_norm': 0.472698450088501, 'train/lr_0': 8.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:33:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:33:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:33:56] train_sft.py:596 - step 4132: {'train/loss': 0.015143729746341705, 'train/grad_norm': 0.4161246120929718, 'train/lr_0': 8.67e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:33:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:33:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:01] train_sft.py:596 - step 4133: {'train/loss': 0.010625235736370087, 'train/grad_norm': 0.4179993271827698, 'train/lr_0': 8.66e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:34:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:34:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:06] train_sft.py:596 - step 4134: {'train/loss': 0.008797235786914825, 'train/grad_norm': 0.22530785202980042, 'train/lr_0': 8.65e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:34:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:34:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:11] train_sft.py:596 - step 4135: {'train/loss': 0.008480217307806015, 'train/grad_norm': 0.20867116749286652, 'train/lr_0': 8.64e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 17:34:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:34:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:15] train_sft.py:596 - step 4136: {'train/loss': 0.002964223735034466, 'train/grad_norm': 0.11795727163553238, 'train/lr_0': 8.63e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 17:34:15] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:34:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:20] train_sft.py:596 - step 4137: {'train/loss': 0.009232841432094574, 'train/grad_norm': 0.3708886504173279, 'train/lr_0': 8.62e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:34:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:34:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:25] train_sft.py:596 - step 4138: {'train/loss': 0.008605867624282837, 'train/grad_norm': 0.31945303082466125, 'train/lr_0': 8.61e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:34:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:34:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:29] train_sft.py:596 - step 4139: {'train/loss': 0.011316675692796707, 'train/grad_norm': 0.39580070972442627, 'train/lr_0': 8.6e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:34:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:34:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:34] train_sft.py:596 - step 4140: {'train/loss': 0.012682177126407623, 'train/grad_norm': 0.3563646972179413, 'train/lr_0': 8.59e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 17:34:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:34:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:39] train_sft.py:596 - step 4141: {'train/loss': 0.010931439697742462, 'train/grad_norm': 0.449921190738678, 'train/lr_0': 8.580000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:34:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:34:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:44] train_sft.py:596 - step 4142: {'train/loss': 0.006758391857147217, 'train/grad_norm': 0.25054147839546204, 'train/lr_0': 8.570000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:34:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:34:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:49] train_sft.py:596 - step 4143: {'train/loss': 0.009836636483669281, 'train/grad_norm': 0.5756199955940247, 'train/lr_0': 8.560000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:34:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:34:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:53] train_sft.py:596 - step 4144: {'train/loss': 0.012232042849063873, 'train/grad_norm': 0.41619616746902466, 'train/lr_0': 8.550000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:34:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:34:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:34:58] train_sft.py:596 - step 4145: {'train/loss': 0.009965913370251656, 'train/grad_norm': 0.31259915232658386, 'train/lr_0': 8.540000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:34:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:34:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:03] train_sft.py:596 - step 4146: {'train/loss': 0.011912703514099121, 'train/grad_norm': 0.41110703349113464, 'train/lr_0': 8.530000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:35:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:35:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:08] train_sft.py:596 - step 4147: {'train/loss': 0.009864792227745056, 'train/grad_norm': 0.2750222980976105, 'train/lr_0': 8.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:35:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:35:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:12] train_sft.py:596 - step 4148: {'train/loss': 0.009851522743701935, 'train/grad_norm': 0.33154359459877014, 'train/lr_0': 8.51e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:35:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:35:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:17] train_sft.py:596 - step 4149: {'train/loss': 0.008475681766867638, 'train/grad_norm': 0.5624470710754395, 'train/lr_0': 8.5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 17:35:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:35:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:21] train_sft.py:596 - step 4150: {'train/loss': 0.009207889437675476, 'train/grad_norm': 0.2948424816131592, 'train/lr_0': 8.49e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:35:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:35:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:26] train_sft.py:596 - step 4151: {'train/loss': 0.010749049484729767, 'train/grad_norm': 0.2619948983192444, 'train/lr_0': 8.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:35:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:35:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:31] train_sft.py:596 - step 4152: {'train/loss': 0.02006165962666273, 'train/grad_norm': 0.611058235168457, 'train/lr_0': 8.47e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:35:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:35:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:36] train_sft.py:596 - step 4153: {'train/loss': 0.012615516781806946, 'train/grad_norm': 0.49723273515701294, 'train/lr_0': 8.46e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:35:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:35:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:41] train_sft.py:596 - step 4154: {'train/loss': 0.009963162243366241, 'train/grad_norm': 0.2943231165409088, 'train/lr_0': 8.45e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 17:35:41] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:35:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:46] train_sft.py:596 - step 4155: {'train/loss': 0.010989166796207428, 'train/grad_norm': 0.39107444882392883, 'train/lr_0': 8.44e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 17:35:46] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:35:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:51] train_sft.py:596 - step 4156: {'train/loss': 0.012666285037994385, 'train/grad_norm': 0.4840896725654602, 'train/lr_0': 8.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:35:51] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:35:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:35:55] train_sft.py:596 - step 4157: {'train/loss': 0.027175702154636383, 'train/grad_norm': 0.5729556083679199, 'train/lr_0': 8.42e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:35:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:35:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:00] train_sft.py:596 - step 4158: {'train/loss': 0.01048295944929123, 'train/grad_norm': 0.5770893692970276, 'train/lr_0': 8.41e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:36:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:36:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:05] train_sft.py:596 - step 4159: {'train/loss': 0.010916821658611298, 'train/grad_norm': 0.4570538401603699, 'train/lr_0': 8.400000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:36:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:36:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:10] train_sft.py:596 - step 4160: {'train/loss': 0.01311531662940979, 'train/grad_norm': 0.4657398760318756, 'train/lr_0': 8.390000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:36:10] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:36:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:15] train_sft.py:596 - step 4161: {'train/loss': 0.008230060338973999, 'train/grad_norm': 0.25030818581581116, 'train/lr_0': 8.380000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:36:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:36:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:19] train_sft.py:596 - step 4162: {'train/loss': 0.01134570688009262, 'train/grad_norm': 0.6601702570915222, 'train/lr_0': 8.370000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 17:36:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:36:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:24] train_sft.py:596 - step 4163: {'train/loss': 0.009284567087888718, 'train/grad_norm': 0.27653515338897705, 'train/lr_0': 8.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 17:36:24] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:36:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:29] train_sft.py:596 - step 4164: {'train/loss': 0.007608853280544281, 'train/grad_norm': 0.23685157299041748, 'train/lr_0': 8.35e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:36:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:36:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:34] train_sft.py:596 - step 4165: {'train/loss': 0.007894918322563171, 'train/grad_norm': 0.2702632546424866, 'train/lr_0': 8.34e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:36:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:36:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:39] train_sft.py:596 - step 4166: {'train/loss': 0.00913509726524353, 'train/grad_norm': 0.40184128284454346, 'train/lr_0': 8.33e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 17:36:39] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 17:36:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:44] train_sft.py:596 - step 4167: {'train/loss': 0.013000667095184326, 'train/grad_norm': 0.34441283345222473, 'train/lr_0': 8.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:36:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:36:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:49] train_sft.py:596 - step 4168: {'train/loss': 0.01243409514427185, 'train/grad_norm': 0.3348182141780853, 'train/lr_0': 8.31e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 17:36:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:36:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:54] train_sft.py:596 - step 4169: {'train/loss': 0.009891793131828308, 'train/grad_norm': 0.30092376470565796, 'train/lr_0': 8.3e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:36:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:36:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:36:59] train_sft.py:596 - step 4170: {'train/loss': 0.01076420396566391, 'train/grad_norm': 0.307603120803833, 'train/lr_0': 8.29e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:36:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:36:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:04] train_sft.py:596 - step 4171: {'train/loss': 0.019498035311698914, 'train/grad_norm': 0.40862762928009033, 'train/lr_0': 8.280000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:37:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:37:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:08] train_sft.py:596 - step 4172: {'train/loss': 0.005146525800228119, 'train/grad_norm': 0.24071568250656128, 'train/lr_0': 8.27e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:37:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:37:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:13] train_sft.py:596 - step 4173: {'train/loss': 0.009527100250124931, 'train/grad_norm': 0.45208999514579773, 'train/lr_0': 8.26e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:37:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:37:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:18] train_sft.py:596 - step 4174: {'train/loss': 0.008210757747292519, 'train/grad_norm': 0.3136025667190552, 'train/lr_0': 8.25e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 17:37:18] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:37:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:23] train_sft.py:596 - step 4175: {'train/loss': 0.011738931760191917, 'train/grad_norm': 0.5463184714317322, 'train/lr_0': 8.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 17:37:23] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:37:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:27] train_sft.py:596 - step 4176: {'train/loss': 0.0074407681822776794, 'train/grad_norm': 0.38741031289100647, 'train/lr_0': 8.23e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.21s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.21s/it]
[2025-12-29 17:37:27] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 17:37:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:32] train_sft.py:596 - step 4177: {'train/loss': 0.009152621030807495, 'train/grad_norm': 0.28374582529067993, 'train/lr_0': 8.220000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 17:37:32] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:37:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:37] train_sft.py:596 - step 4178: {'train/loss': 0.005830615758895874, 'train/grad_norm': 0.15740878880023956, 'train/lr_0': 8.210000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:37:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:37:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:42] train_sft.py:596 - step 4179: {'train/loss': 0.008829252794384956, 'train/grad_norm': 0.1597515493631363, 'train/lr_0': 8.200000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:37:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:37:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:46] train_sft.py:596 - step 4180: {'train/loss': 0.018528953194618225, 'train/grad_norm': 0.4345177412033081, 'train/lr_0': 8.19e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:37:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:37:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:51] train_sft.py:596 - step 4181: {'train/loss': 0.009197242558002472, 'train/grad_norm': 0.5645342469215393, 'train/lr_0': 8.180000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:37:51] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:37:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:37:56] train_sft.py:596 - step 4182: {'train/loss': 0.007520705461502075, 'train/grad_norm': 0.233749657869339, 'train/lr_0': 8.17e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 17:37:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:37:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:01] train_sft.py:596 - step 4183: {'train/loss': 0.0085049569606781, 'train/grad_norm': 0.30369964241981506, 'train/lr_0': 8.160000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:38:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:38:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:05] train_sft.py:596 - step 4184: {'train/loss': 0.007085941731929779, 'train/grad_norm': 0.2928869426250458, 'train/lr_0': 8.15e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:38:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:38:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:10] train_sft.py:596 - step 4185: {'train/loss': 0.01705886423587799, 'train/grad_norm': 0.5688602328300476, 'train/lr_0': 8.140000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:38:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:38:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:15] train_sft.py:596 - step 4186: {'train/loss': 0.008194200694561005, 'train/grad_norm': 0.29628321528434753, 'train/lr_0': 8.13e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:38:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:38:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:19] train_sft.py:596 - step 4187: {'train/loss': 0.0078117139637470245, 'train/grad_norm': 0.3039766252040863, 'train/lr_0': 8.120000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:38:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:38:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:25] train_sft.py:596 - step 4188: {'train/loss': 0.008303716778755188, 'train/grad_norm': 0.39454519748687744, 'train/lr_0': 8.11e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 17:38:25] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:38:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:29] train_sft.py:596 - step 4189: {'train/loss': 0.009662207216024399, 'train/grad_norm': 0.2445087432861328, 'train/lr_0': 8.1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:38:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:38:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:34] train_sft.py:596 - step 4190: {'train/loss': 0.006293982267379761, 'train/grad_norm': 0.2414632886648178, 'train/lr_0': 8.09e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:38:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:38:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:39] train_sft.py:596 - step 4191: {'train/loss': 0.012642048299312592, 'train/grad_norm': 0.22649288177490234, 'train/lr_0': 8.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 17:38:39] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:38:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:44] train_sft.py:596 - step 4192: {'train/loss': 0.008480586111545563, 'train/grad_norm': 0.3359614908695221, 'train/lr_0': 8.07e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 17:38:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:38:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:48] train_sft.py:596 - step 4193: {'train/loss': 0.007296696305274963, 'train/grad_norm': 0.21201281249523163, 'train/lr_0': 8.06e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:38:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:38:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:54] train_sft.py:596 - step 4194: {'train/loss': 0.010257355868816376, 'train/grad_norm': 0.31926965713500977, 'train/lr_0': 8.050000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 17:38:54] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:38:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:38:59] train_sft.py:596 - step 4195: {'train/loss': 0.012547817081212997, 'train/grad_norm': 0.5602242350578308, 'train/lr_0': 8.040000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 17:38:59] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:38:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:03] train_sft.py:596 - step 4196: {'train/loss': 0.013829555362462997, 'train/grad_norm': 1.0139961242675781, 'train/lr_0': 8.03e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:39:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:39:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:08] train_sft.py:596 - step 4197: {'train/loss': 0.00754932314157486, 'train/grad_norm': 0.3651703894138336, 'train/lr_0': 8.020000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:39:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:39:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:13] train_sft.py:596 - step 4198: {'train/loss': 0.01218201220035553, 'train/grad_norm': 0.6753197312355042, 'train/lr_0': 8.01e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:39:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:39:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:18] train_sft.py:596 - step 4199: {'train/loss': 0.01571238785982132, 'train/grad_norm': 0.5633041858673096, 'train/lr_0': 8.000000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:39:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:39:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:22] train_sft.py:596 - step 4200: {'train/loss': 0.030645007267594337, 'train/grad_norm': 0.813542366027832, 'train/lr_0': 7.99e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:39:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:39:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:27] train_sft.py:596 - step 4201: {'train/loss': 0.013016298413276672, 'train/grad_norm': 0.6993520855903625, 'train/lr_0': 7.980000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:39:27] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:39:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:32] train_sft.py:596 - step 4202: {'train/loss': 0.01689666509628296, 'train/grad_norm': 0.5136730074882507, 'train/lr_0': 7.97e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:39:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:39:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:37] train_sft.py:596 - step 4203: {'train/loss': 0.01843520998954773, 'train/grad_norm': 0.7254404425621033, 'train/lr_0': 7.960000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:39:37] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:39:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:42] train_sft.py:596 - step 4204: {'train/loss': 0.006504550576210022, 'train/grad_norm': 0.2817935645580292, 'train/lr_0': 7.950000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:39:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:39:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:46] train_sft.py:596 - step 4205: {'train/loss': 0.01477719098329544, 'train/grad_norm': 0.38676199316978455, 'train/lr_0': 7.94e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:39:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:39:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:51] train_sft.py:596 - step 4206: {'train/loss': 0.01215505599975586, 'train/grad_norm': 0.41069313883781433, 'train/lr_0': 7.93e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:39:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:39:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:39:56] train_sft.py:596 - step 4207: {'train/loss': 0.01109623908996582, 'train/grad_norm': 0.5089394450187683, 'train/lr_0': 7.92e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 17:39:56] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:39:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:01] train_sft.py:596 - step 4208: {'train/loss': 0.011591194197535515, 'train/grad_norm': 0.4088507890701294, 'train/lr_0': 7.91e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 17:40:01] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:40:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:06] train_sft.py:596 - step 4209: {'train/loss': 0.009791143238544464, 'train/grad_norm': 0.3555351793766022, 'train/lr_0': 7.9e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 17:40:06] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:40:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:11] train_sft.py:596 - step 4210: {'train/loss': 0.013327732682228088, 'train/grad_norm': 0.6192372441291809, 'train/lr_0': 7.89e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:40:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:40:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:16] train_sft.py:596 - step 4211: {'train/loss': 0.01058880239725113, 'train/grad_norm': 0.4027233421802521, 'train/lr_0': 7.88e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:40:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:40:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:20] train_sft.py:596 - step 4212: {'train/loss': 0.013175442814826965, 'train/grad_norm': 0.29805904626846313, 'train/lr_0': 7.870000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:40:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:40:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:25] train_sft.py:596 - step 4213: {'train/loss': 0.006952852010726929, 'train/grad_norm': 0.2979230582714081, 'train/lr_0': 7.860000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:40:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:40:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:30] train_sft.py:596 - step 4214: {'train/loss': 0.009405001997947693, 'train/grad_norm': 0.5525352954864502, 'train/lr_0': 7.850000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 17:40:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:40:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:34] train_sft.py:596 - step 4215: {'train/loss': 0.005147334188222885, 'train/grad_norm': 0.16544665396213531, 'train/lr_0': 7.840000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 17:40:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:40:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:39] train_sft.py:596 - step 4216: {'train/loss': 0.0071242451667785645, 'train/grad_norm': 0.20733240246772766, 'train/lr_0': 7.830000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:40:39] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:40:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:44] train_sft.py:596 - step 4217: {'train/loss': 0.015245199203491211, 'train/grad_norm': 0.4924475848674774, 'train/lr_0': 7.820000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:40:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:40:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:48] train_sft.py:596 - step 4218: {'train/loss': 0.008075729012489319, 'train/grad_norm': 0.2695600092411041, 'train/lr_0': 7.810000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:40:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:40:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:53] train_sft.py:596 - step 4219: {'train/loss': 0.01472868025302887, 'train/grad_norm': 0.4011850655078888, 'train/lr_0': 7.800000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:40:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:40:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:40:58] train_sft.py:596 - step 4220: {'train/loss': 0.009874433279037476, 'train/grad_norm': 0.37849879264831543, 'train/lr_0': 7.790000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:40:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:40:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:03] train_sft.py:596 - step 4221: {'train/loss': 0.008985646069049835, 'train/grad_norm': 0.5709028244018555, 'train/lr_0': 7.78e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 17:41:03] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:41:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:08] train_sft.py:596 - step 4222: {'train/loss': 0.018509775400161743, 'train/grad_norm': 0.8848804235458374, 'train/lr_0': 7.77e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 17:41:08] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:41:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:13] train_sft.py:596 - step 4223: {'train/loss': 0.009223364293575287, 'train/grad_norm': 0.29517480731010437, 'train/lr_0': 7.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:41:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:41:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:17] train_sft.py:596 - step 4224: {'train/loss': 0.01686940062791109, 'train/grad_norm': 0.39059028029441833, 'train/lr_0': 7.75e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:41:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:41:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:22] train_sft.py:596 - step 4225: {'train/loss': 0.007023759186267853, 'train/grad_norm': 0.18613523244857788, 'train/lr_0': 7.74e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:41:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:41:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:27] train_sft.py:596 - step 4226: {'train/loss': 0.006564632058143616, 'train/grad_norm': 0.1449824571609497, 'train/lr_0': 7.73e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:41:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:41:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:32] train_sft.py:596 - step 4227: {'train/loss': 0.006692111492156982, 'train/grad_norm': 0.30689945816993713, 'train/lr_0': 7.72e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:41:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:41:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:37] train_sft.py:596 - step 4228: {'train/loss': 0.011884160339832306, 'train/grad_norm': 0.4699258506298065, 'train/lr_0': 7.71e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:41:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:41:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:42] train_sft.py:596 - step 4229: {'train/loss': 0.010688677430152893, 'train/grad_norm': 0.41977936029434204, 'train/lr_0': 7.7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:41:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:41:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:46] train_sft.py:596 - step 4230: {'train/loss': 0.006788969039916992, 'train/grad_norm': 0.20923049747943878, 'train/lr_0': 7.690000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:41:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:41:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:51] train_sft.py:596 - step 4231: {'train/loss': 0.008055128157138824, 'train/grad_norm': 0.3780374825000763, 'train/lr_0': 7.680000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 17:41:51] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 17:41:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:41:56] train_sft.py:596 - step 4232: {'train/loss': 0.009218789637088776, 'train/grad_norm': 0.2743455767631531, 'train/lr_0': 7.670000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:41:56] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:41:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:00] train_sft.py:596 - step 4233: {'train/loss': 0.007998615503311157, 'train/grad_norm': 0.2687392830848694, 'train/lr_0': 7.660000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:42:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:42:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:05] train_sft.py:596 - step 4234: {'train/loss': 0.010815979912877083, 'train/grad_norm': 0.4032990634441376, 'train/lr_0': 7.650000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 17:42:05] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:42:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:10] train_sft.py:596 - step 4235: {'train/loss': 0.011048626678530127, 'train/grad_norm': 0.3252338767051697, 'train/lr_0': 7.640000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:42:10] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:42:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:15] train_sft.py:596 - step 4236: {'train/loss': 0.013102564960718155, 'train/grad_norm': 0.47574207186698914, 'train/lr_0': 7.630000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 17:42:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:42:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:19] train_sft.py:596 - step 4237: {'train/loss': 0.014757618308067322, 'train/grad_norm': 0.44665688276290894, 'train/lr_0': 7.620000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:42:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:42:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:24] train_sft.py:596 - step 4238: {'train/loss': 0.008318593725562096, 'train/grad_norm': 0.2127738893032074, 'train/lr_0': 7.610000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:42:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:42:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:28] train_sft.py:596 - step 4239: {'train/loss': 0.01096533052623272, 'train/grad_norm': 0.7097073197364807, 'train/lr_0': 7.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:42:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:42:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:34] train_sft.py:596 - step 4240: {'train/loss': 0.015228241682052612, 'train/grad_norm': 0.4903281629085541, 'train/lr_0': 7.590000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 17:42:34] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:42:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:38] train_sft.py:596 - step 4241: {'train/loss': 0.008575059473514557, 'train/grad_norm': 0.23726558685302734, 'train/lr_0': 7.58e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:42:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:42:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:43] train_sft.py:596 - step 4242: {'train/loss': 0.012636058032512665, 'train/grad_norm': 0.354166179895401, 'train/lr_0': 7.57e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:42:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:42:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:48] train_sft.py:596 - step 4243: {'train/loss': 0.012333802878856659, 'train/grad_norm': 0.4525318145751953, 'train/lr_0': 7.5600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 17:42:48] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:42:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:53] train_sft.py:596 - step 4244: {'train/loss': 0.0157153457403183, 'train/grad_norm': 0.5388616919517517, 'train/lr_0': 7.5500000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:42:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:42:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:42:57] train_sft.py:596 - step 4245: {'train/loss': 0.013062912970781326, 'train/grad_norm': 0.44339799880981445, 'train/lr_0': 7.540000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:42:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:42:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:02] train_sft.py:596 - step 4246: {'train/loss': 0.009480640292167664, 'train/grad_norm': 0.3472624123096466, 'train/lr_0': 7.530000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:43:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:43:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:07] train_sft.py:596 - step 4247: {'train/loss': 0.010998200625181198, 'train/grad_norm': 0.4064503014087677, 'train/lr_0': 7.520000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 17:43:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:43:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:12] train_sft.py:596 - step 4248: {'train/loss': 0.009365521371364594, 'train/grad_norm': 0.2522438168525696, 'train/lr_0': 7.510000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:43:12] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:43:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:16] train_sft.py:596 - step 4249: {'train/loss': 0.010162968188524246, 'train/grad_norm': 0.4446297585964203, 'train/lr_0': 7.500000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:43:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:22] train_sft.py:596 - step 4250: {'train/loss': 0.007690083235502243, 'train/grad_norm': 0.2425825595855713, 'train/lr_0': 7.49e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.02s/it]
[2025-12-29 17:43:22] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 17:43:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:26] train_sft.py:596 - step 4251: {'train/loss': 0.010603271424770355, 'train/grad_norm': 0.28641897439956665, 'train/lr_0': 7.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:43:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:31] train_sft.py:596 - step 4252: {'train/loss': 0.006048381328582764, 'train/grad_norm': 0.1499509960412979, 'train/lr_0': 7.4700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:43:31] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:43:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:36] train_sft.py:596 - step 4253: {'train/loss': 0.011944219470024109, 'train/grad_norm': 0.31361350417137146, 'train/lr_0': 7.4600000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:43:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:40] train_sft.py:596 - step 4254: {'train/loss': 0.010038025677204132, 'train/grad_norm': 0.4786357283592224, 'train/lr_0': 7.450000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:43:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:45] train_sft.py:596 - step 4255: {'train/loss': 0.009283307939767838, 'train/grad_norm': 0.20885245501995087, 'train/lr_0': 7.440000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:43:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:50] train_sft.py:596 - step 4256: {'train/loss': 0.00656774640083313, 'train/grad_norm': 0.296456515789032, 'train/lr_0': 7.430000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:43:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:54] train_sft.py:596 - step 4257: {'train/loss': 0.013382509350776672, 'train/grad_norm': 0.3579937815666199, 'train/lr_0': 7.420000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:43:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:43:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:43:59] train_sft.py:596 - step 4258: {'train/loss': 0.012208469212055206, 'train/grad_norm': 0.4011543095111847, 'train/lr_0': 7.41e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:43:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:43:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:03] train_sft.py:596 - step 4259: {'train/loss': 0.01365276426076889, 'train/grad_norm': 0.47197824716567993, 'train/lr_0': 7.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:44:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:44:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:08] train_sft.py:596 - step 4260: {'train/loss': 0.01614847034215927, 'train/grad_norm': 0.24602043628692627, 'train/lr_0': 7.39e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:44:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:44:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:13] train_sft.py:596 - step 4261: {'train/loss': 0.01320456713438034, 'train/grad_norm': 0.45236921310424805, 'train/lr_0': 7.3800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:44:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:44:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:17] train_sft.py:596 - step 4262: {'train/loss': 0.007224366068840027, 'train/grad_norm': 0.4720980226993561, 'train/lr_0': 7.370000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:44:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:44:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:22] train_sft.py:596 - step 4263: {'train/loss': 0.008260466158390045, 'train/grad_norm': 0.25147107243537903, 'train/lr_0': 7.360000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:44:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:44:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:27] train_sft.py:596 - step 4264: {'train/loss': 0.009021550416946411, 'train/grad_norm': 0.3018861711025238, 'train/lr_0': 7.350000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:44:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:44:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:31] train_sft.py:596 - step 4265: {'train/loss': 0.00730443000793457, 'train/grad_norm': 0.2063116878271103, 'train/lr_0': 7.340000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 17:44:31] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:44:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:36] train_sft.py:596 - step 4266: {'train/loss': 0.011235421523451805, 'train/grad_norm': 0.7192175388336182, 'train/lr_0': 7.33e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:44:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:44:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:41] train_sft.py:596 - step 4267: {'train/loss': 0.007476819679141045, 'train/grad_norm': 0.2735595703125, 'train/lr_0': 7.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:44:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:44:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:45] train_sft.py:596 - step 4268: {'train/loss': 0.006823442876338959, 'train/grad_norm': 0.18302130699157715, 'train/lr_0': 7.31e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:44:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:44:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:50] train_sft.py:596 - step 4269: {'train/loss': 0.008831962943077087, 'train/grad_norm': 0.5555351376533508, 'train/lr_0': 7.3e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:44:50] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:44:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:55] train_sft.py:596 - step 4270: {'train/loss': 0.008865002542734146, 'train/grad_norm': 0.26613128185272217, 'train/lr_0': 7.2900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:44:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:44:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:44:59] train_sft.py:596 - step 4271: {'train/loss': 0.01435038447380066, 'train/grad_norm': 0.6933398246765137, 'train/lr_0': 7.280000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 17:44:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:45:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:04] train_sft.py:596 - step 4272: {'train/loss': 0.010264310985803604, 'train/grad_norm': 0.7262053489685059, 'train/lr_0': 7.270000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:45:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:45:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:09] train_sft.py:596 - step 4273: {'train/loss': 0.014779273420572281, 'train/grad_norm': 0.43607890605926514, 'train/lr_0': 7.260000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:45:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:45:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:13] train_sft.py:596 - step 4274: {'train/loss': 0.01254916563630104, 'train/grad_norm': 0.4874131977558136, 'train/lr_0': 7.25e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:45:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:45:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:18] train_sft.py:596 - step 4275: {'train/loss': 0.010590283200144768, 'train/grad_norm': 0.20354852080345154, 'train/lr_0': 7.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:45:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:45:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:23] train_sft.py:596 - step 4276: {'train/loss': 0.006182163953781128, 'train/grad_norm': 0.1450747400522232, 'train/lr_0': 7.23e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.92s/it]
[2025-12-29 17:45:23] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:45:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:28] train_sft.py:596 - step 4277: {'train/loss': 0.00804811716079712, 'train/grad_norm': 0.26743370294570923, 'train/lr_0': 7.22e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 17:45:28] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:45:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:33] train_sft.py:596 - step 4278: {'train/loss': 0.01370362937450409, 'train/grad_norm': 0.2793819010257721, 'train/lr_0': 7.2100000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:45:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:45:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:37] train_sft.py:596 - step 4279: {'train/loss': 0.006908878684043884, 'train/grad_norm': 0.3398585021495819, 'train/lr_0': 7.2000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 17:45:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:45:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:42] train_sft.py:596 - step 4280: {'train/loss': 0.006075829267501831, 'train/grad_norm': 0.16013993322849274, 'train/lr_0': 7.190000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:45:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:45:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:47] train_sft.py:596 - step 4281: {'train/loss': 0.011504948139190674, 'train/grad_norm': 0.4063663184642792, 'train/lr_0': 7.180000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:45:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:45:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:52] train_sft.py:596 - step 4282: {'train/loss': 0.005284711718559265, 'train/grad_norm': 0.23299935460090637, 'train/lr_0': 7.170000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:45:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:45:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:45:57] train_sft.py:596 - step 4283: {'train/loss': 0.008066609501838684, 'train/grad_norm': 0.2723376750946045, 'train/lr_0': 7.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:45:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:45:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:01] train_sft.py:596 - step 4284: {'train/loss': 0.006116990000009537, 'train/grad_norm': 0.33239275217056274, 'train/lr_0': 7.150000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:46:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:46:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:06] train_sft.py:596 - step 4285: {'train/loss': 0.00925547443330288, 'train/grad_norm': 0.2866339385509491, 'train/lr_0': 7.14e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:46:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:46:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:10] train_sft.py:596 - step 4286: {'train/loss': 0.003032846376299858, 'train/grad_norm': 0.1028580367565155, 'train/lr_0': 7.130000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:46:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:46:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:15] train_sft.py:596 - step 4287: {'train/loss': 0.010867767035961151, 'train/grad_norm': 0.46581223607063293, 'train/lr_0': 7.1200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:46:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:46:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:20] train_sft.py:596 - step 4288: {'train/loss': 0.006916306912899017, 'train/grad_norm': 0.17747189104557037, 'train/lr_0': 7.110000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:46:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:46:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:24] train_sft.py:596 - step 4289: {'train/loss': 0.012881182134151459, 'train/grad_norm': 0.27416324615478516, 'train/lr_0': 7.100000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:46:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:46:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:29] train_sft.py:596 - step 4290: {'train/loss': 0.007552005350589752, 'train/grad_norm': 0.2390347272157669, 'train/lr_0': 7.0900000000000016e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:46:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:46:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:34] train_sft.py:596 - step 4291: {'train/loss': 0.008079588413238525, 'train/grad_norm': 0.5537970066070557, 'train/lr_0': 7.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 17:46:34] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:46:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:39] train_sft.py:596 - step 4292: {'train/loss': 0.009097143076360226, 'train/grad_norm': 0.5631456971168518, 'train/lr_0': 7.070000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:46:39] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:46:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:44] train_sft.py:596 - step 4293: {'train/loss': 0.01721089519560337, 'train/grad_norm': 0.8924562335014343, 'train/lr_0': 7.06e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:46:44] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:46:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:48] train_sft.py:596 - step 4294: {'train/loss': 0.0034953253343701363, 'train/grad_norm': 0.31373852491378784, 'train/lr_0': 7.050000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:46:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:46:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:53] train_sft.py:596 - step 4295: {'train/loss': 0.005925461649894714, 'train/grad_norm': 0.25648319721221924, 'train/lr_0': 7.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:46:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:46:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:46:58] train_sft.py:596 - step 4296: {'train/loss': 0.0058196354657411575, 'train/grad_norm': 0.2257128804922104, 'train/lr_0': 7.030000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:46:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:46:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:03] train_sft.py:596 - step 4297: {'train/loss': 0.013945169746875763, 'train/grad_norm': 0.6597194075584412, 'train/lr_0': 7.0200000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 17:47:03] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:47:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:07] train_sft.py:596 - step 4298: {'train/loss': 0.009096160531044006, 'train/grad_norm': 0.40063315629959106, 'train/lr_0': 7.0100000000000015e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:47:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:47:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:12] train_sft.py:596 - step 4299: {'train/loss': 0.007291488349437714, 'train/grad_norm': 0.5533086061477661, 'train/lr_0': 7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
[2025-12-29 17:47:12] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 17:47:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:17] train_sft.py:596 - step 4300: {'train/loss': 0.011259384453296661, 'train/grad_norm': 0.34639471769332886, 'train/lr_0': 6.990000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:47:17] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:47:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:21] train_sft.py:596 - step 4301: {'train/loss': 0.007925055921077728, 'train/grad_norm': 0.3464180529117584, 'train/lr_0': 6.98e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:47:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:47:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:27] train_sft.py:596 - step 4302: {'train/loss': 0.0043386295437812805, 'train/grad_norm': 0.22905072569847107, 'train/lr_0': 6.970000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.23s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.23s/it]
[2025-12-29 17:47:27] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 17:47:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:32] train_sft.py:596 - step 4303: {'train/loss': 0.01153072714805603, 'train/grad_norm': 0.5294004082679749, 'train/lr_0': 6.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:47:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:47:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:37] train_sft.py:596 - step 4304: {'train/loss': 0.003119034692645073, 'train/grad_norm': 0.11519311368465424, 'train/lr_0': 6.950000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:47:37] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:47:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:41] train_sft.py:596 - step 4305: {'train/loss': 0.004171050153672695, 'train/grad_norm': 0.22891168296337128, 'train/lr_0': 6.9400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:47:41] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:47:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:46] train_sft.py:596 - step 4306: {'train/loss': 0.0026208609342575073, 'train/grad_norm': 0.08126534521579742, 'train/lr_0': 6.930000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:47:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:47:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:51] train_sft.py:596 - step 4307: {'train/loss': 0.0030557960271835327, 'train/grad_norm': 0.2750976085662842, 'train/lr_0': 6.92e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:47:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:47:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:47:56] train_sft.py:596 - step 4308: {'train/loss': 0.0051579102873802185, 'train/grad_norm': 0.12667031586170197, 'train/lr_0': 6.910000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:47:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:47:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:00] train_sft.py:596 - step 4309: {'train/loss': 0.010196411982178688, 'train/grad_norm': 0.3431883454322815, 'train/lr_0': 6.9e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:48:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:48:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:05] train_sft.py:596 - step 4310: {'train/loss': 0.005825363099575043, 'train/grad_norm': 0.24601462483406067, 'train/lr_0': 6.890000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:48:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:48:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:10] train_sft.py:596 - step 4311: {'train/loss': 0.0073783546686172485, 'train/grad_norm': 0.4062138497829437, 'train/lr_0': 6.88e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 17:48:10] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:48:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:15] train_sft.py:596 - step 4312: {'train/loss': 0.0051370225846767426, 'train/grad_norm': 0.19855357706546783, 'train/lr_0': 6.870000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:48:15] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:48:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:20] train_sft.py:596 - step 4313: {'train/loss': 0.004006888717412949, 'train/grad_norm': 0.17874811589717865, 'train/lr_0': 6.86e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:48:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:48:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:25] train_sft.py:596 - step 4314: {'train/loss': 0.0048826634883880615, 'train/grad_norm': 0.19984254240989685, 'train/lr_0': 6.850000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:48:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:48:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:29] train_sft.py:596 - step 4315: {'train/loss': 0.004200860857963562, 'train/grad_norm': 0.12891380488872528, 'train/lr_0': 6.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 17:48:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:48:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:34] train_sft.py:596 - step 4316: {'train/loss': 0.008684132248163223, 'train/grad_norm': 0.28321146965026855, 'train/lr_0': 6.830000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:48:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:48:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:39] train_sft.py:596 - step 4317: {'train/loss': 0.00289028137922287, 'train/grad_norm': 0.08763277530670166, 'train/lr_0': 6.82e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:48:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:48:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:44] train_sft.py:596 - step 4318: {'train/loss': 0.010153360664844513, 'train/grad_norm': 0.3565772771835327, 'train/lr_0': 6.810000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:48:44] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:48:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:48] train_sft.py:596 - step 4319: {'train/loss': 0.00446455180644989, 'train/grad_norm': 0.1446780264377594, 'train/lr_0': 6.8e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:48:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:48:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:53] train_sft.py:596 - step 4320: {'train/loss': 0.007554303854703903, 'train/grad_norm': 0.5820258855819702, 'train/lr_0': 6.790000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:48:53] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:48:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:48:57] train_sft.py:596 - step 4321: {'train/loss': 0.005200937390327454, 'train/grad_norm': 0.23614619672298431, 'train/lr_0': 6.78e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:48:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:48:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:02] train_sft.py:596 - step 4322: {'train/loss': 0.007562622427940369, 'train/grad_norm': 0.2961398959159851, 'train/lr_0': 6.770000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:49:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:49:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:07] train_sft.py:596 - step 4323: {'train/loss': 0.007380682043731213, 'train/grad_norm': 0.3420158624649048, 'train/lr_0': 6.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:49:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:11] train_sft.py:596 - step 4324: {'train/loss': 0.012798890471458435, 'train/grad_norm': 0.521831750869751, 'train/lr_0': 6.750000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:49:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:16] train_sft.py:596 - step 4325: {'train/loss': 0.0037163756787776947, 'train/grad_norm': 0.1338183581829071, 'train/lr_0': 6.74e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:49:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:49:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:21] train_sft.py:596 - step 4326: {'train/loss': 0.00949462503194809, 'train/grad_norm': 0.31580591201782227, 'train/lr_0': 6.730000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:49:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:26] train_sft.py:596 - step 4327: {'train/loss': 0.011826664209365845, 'train/grad_norm': 0.30669739842414856, 'train/lr_0': 6.72e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 17:49:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:49:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:30] train_sft.py:596 - step 4328: {'train/loss': 0.012402141466736794, 'train/grad_norm': 0.21984587609767914, 'train/lr_0': 6.710000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:49:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:35] train_sft.py:596 - step 4329: {'train/loss': 0.007472451776266098, 'train/grad_norm': 0.17845261096954346, 'train/lr_0': 6.7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:49:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:40] train_sft.py:596 - step 4330: {'train/loss': 0.008372136391699314, 'train/grad_norm': 0.4077351689338684, 'train/lr_0': 6.690000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:49:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:44] train_sft.py:596 - step 4331: {'train/loss': 0.008161678910255432, 'train/grad_norm': 0.6001142263412476, 'train/lr_0': 6.6799999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:49:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:49:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:49] train_sft.py:596 - step 4332: {'train/loss': 0.0025884341448545456, 'train/grad_norm': 0.15466490387916565, 'train/lr_0': 6.6700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:49:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:49:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:54] train_sft.py:596 - step 4333: {'train/loss': 0.011386540485545993, 'train/grad_norm': 0.43156927824020386, 'train/lr_0': 6.66e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:49:54] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:49:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:49:58] train_sft.py:596 - step 4334: {'train/loss': 0.010509993880987167, 'train/grad_norm': 0.17078565061092377, 'train/lr_0': 6.650000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:49:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:49:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:03] train_sft.py:596 - step 4335: {'train/loss': 0.008200719952583313, 'train/grad_norm': 0.34803223609924316, 'train/lr_0': 6.64e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:50:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:50:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:08] train_sft.py:596 - step 4336: {'train/loss': 0.009928122162818909, 'train/grad_norm': 0.2778526246547699, 'train/lr_0': 6.630000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 17:50:08] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 17:50:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:13] train_sft.py:596 - step 4337: {'train/loss': 0.00683390349149704, 'train/grad_norm': 0.24547047913074493, 'train/lr_0': 6.62e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:50:13] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:50:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:18] train_sft.py:596 - step 4338: {'train/loss': 0.006732195615768433, 'train/grad_norm': 0.2225847840309143, 'train/lr_0': 6.610000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:50:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:50:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:22] train_sft.py:596 - step 4339: {'train/loss': 0.007795393466949463, 'train/grad_norm': 0.17417901754379272, 'train/lr_0': 6.5999999999999995e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:50:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:50:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:27] train_sft.py:596 - step 4340: {'train/loss': 0.007888462394475937, 'train/grad_norm': 0.2133844643831253, 'train/lr_0': 6.5900000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:50:27] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:50:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:32] train_sft.py:596 - step 4341: {'train/loss': 0.013131859479472041, 'train/grad_norm': 0.5333990454673767, 'train/lr_0': 6.58e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:50:32] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:50:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:37] train_sft.py:596 - step 4342: {'train/loss': 0.007088009268045425, 'train/grad_norm': 0.35251984000205994, 'train/lr_0': 6.570000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 17:50:37] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:50:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:42] train_sft.py:596 - step 4343: {'train/loss': 0.008275888860225677, 'train/grad_norm': 0.8284763097763062, 'train/lr_0': 6.560000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:50:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:50:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:47] train_sft.py:596 - step 4344: {'train/loss': 0.0071423836052417755, 'train/grad_norm': 0.2741222679615021, 'train/lr_0': 6.550000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 17:50:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:50:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:52] train_sft.py:596 - step 4345: {'train/loss': 0.011160619556903839, 'train/grad_norm': 0.4883478283882141, 'train/lr_0': 6.540000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:50:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:50:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:50:56] train_sft.py:596 - step 4346: {'train/loss': 0.011683033779263496, 'train/grad_norm': 0.3736220598220825, 'train/lr_0': 6.530000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:50:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:50:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:01] train_sft.py:596 - step 4347: {'train/loss': 0.023823976516723633, 'train/grad_norm': 0.46408993005752563, 'train/lr_0': 6.520000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:51:01] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:51:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:06] train_sft.py:596 - step 4348: {'train/loss': 0.00909380428493023, 'train/grad_norm': 0.3297716975212097, 'train/lr_0': 6.51e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:51:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:51:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:11] train_sft.py:596 - step 4349: {'train/loss': 0.008820271119475365, 'train/grad_norm': 0.36697113513946533, 'train/lr_0': 6.5000000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:51:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:51:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:16] train_sft.py:596 - step 4350: {'train/loss': 0.009575597941875458, 'train/grad_norm': 0.2733348309993744, 'train/lr_0': 6.4900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:51:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:51:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:21] train_sft.py:596 - step 4351: {'train/loss': 0.00459897518157959, 'train/grad_norm': 0.19031468033790588, 'train/lr_0': 6.480000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 17:51:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:51:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:26] train_sft.py:596 - step 4352: {'train/loss': 0.007412657141685486, 'train/grad_norm': 0.23582741618156433, 'train/lr_0': 6.470000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:51:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:51:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:31] train_sft.py:596 - step 4353: {'train/loss': 0.008551344275474548, 'train/grad_norm': 0.3724265694618225, 'train/lr_0': 6.460000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 17:51:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:51:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:36] train_sft.py:596 - step 4354: {'train/loss': 0.008807884529232979, 'train/grad_norm': 0.20633898675441742, 'train/lr_0': 6.450000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 17:51:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:51:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:40] train_sft.py:596 - step 4355: {'train/loss': 0.013861548155546188, 'train/grad_norm': 0.6530795097351074, 'train/lr_0': 6.440000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:51:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:51:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:45] train_sft.py:596 - step 4356: {'train/loss': 0.008013352751731873, 'train/grad_norm': 0.2958093583583832, 'train/lr_0': 6.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 17:51:45] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:51:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:50] train_sft.py:596 - step 4357: {'train/loss': 0.006973555311560631, 'train/grad_norm': 0.2596771717071533, 'train/lr_0': 6.42e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 17:51:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:51:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:51:55] train_sft.py:596 - step 4358: {'train/loss': 0.006809886544942856, 'train/grad_norm': 0.2141549438238144, 'train/lr_0': 6.4100000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 17:51:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:51:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:00] train_sft.py:596 - step 4359: {'train/loss': 0.00969010591506958, 'train/grad_norm': 0.367959588766098, 'train/lr_0': 6.4000000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:52:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:52:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:04] train_sft.py:596 - step 4360: {'train/loss': 0.007420308887958527, 'train/grad_norm': 0.2967749238014221, 'train/lr_0': 6.390000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 17:52:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:52:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:09] train_sft.py:596 - step 4361: {'train/loss': 0.009154558181762695, 'train/grad_norm': 1.0363937616348267, 'train/lr_0': 6.380000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:52:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:52:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:14] train_sft.py:596 - step 4362: {'train/loss': 0.008643046021461487, 'train/grad_norm': 0.3255869448184967, 'train/lr_0': 6.370000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:52:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:52:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:19] train_sft.py:596 - step 4363: {'train/loss': 0.00790015235543251, 'train/grad_norm': 0.7398172616958618, 'train/lr_0': 6.360000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 17:52:19] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:52:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:24] train_sft.py:596 - step 4364: {'train/loss': 0.006652314215898514, 'train/grad_norm': 0.22279049456119537, 'train/lr_0': 6.35e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:52:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:52:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:28] train_sft.py:596 - step 4365: {'train/loss': 0.007473379373550415, 'train/grad_norm': 0.15280140936374664, 'train/lr_0': 6.34e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:52:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:52:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:33] train_sft.py:596 - step 4366: {'train/loss': 0.008588656783103943, 'train/grad_norm': 0.8317838311195374, 'train/lr_0': 6.33e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:52:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:52:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:38] train_sft.py:596 - step 4367: {'train/loss': 0.0077623650431632996, 'train/grad_norm': 0.3365415632724762, 'train/lr_0': 6.3200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:52:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:52:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:42] train_sft.py:596 - step 4368: {'train/loss': 0.011111430823802948, 'train/grad_norm': 0.35328686237335205, 'train/lr_0': 6.3100000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:52:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:52:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:47] train_sft.py:596 - step 4369: {'train/loss': 0.009656473994255066, 'train/grad_norm': 0.33904188871383667, 'train/lr_0': 6.300000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:52:47] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:52:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:52] train_sft.py:596 - step 4370: {'train/loss': 0.004855722188949585, 'train/grad_norm': 0.21446941792964935, 'train/lr_0': 6.290000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:52:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:52:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:52:57] train_sft.py:596 - step 4371: {'train/loss': 0.004535958170890808, 'train/grad_norm': 0.13375595211982727, 'train/lr_0': 6.280000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:52:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:52:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:02] train_sft.py:596 - step 4372: {'train/loss': 0.006538035348057747, 'train/grad_norm': 0.24009044468402863, 'train/lr_0': 6.27e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:53:02] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:53:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:07] train_sft.py:596 - step 4373: {'train/loss': 0.011563815176486969, 'train/grad_norm': 0.5383879542350769, 'train/lr_0': 6.26e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:53:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:53:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:12] train_sft.py:596 - step 4374: {'train/loss': 0.0076025500893592834, 'train/grad_norm': 0.46174144744873047, 'train/lr_0': 6.25e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:53:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:53:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:16] train_sft.py:596 - step 4375: {'train/loss': 0.004946179687976837, 'train/grad_norm': 0.16783075034618378, 'train/lr_0': 6.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:53:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:53:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:21] train_sft.py:596 - step 4376: {'train/loss': 0.004465557634830475, 'train/grad_norm': 0.10380592942237854, 'train/lr_0': 6.2300000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 17:53:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:53:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:26] train_sft.py:596 - step 4377: {'train/loss': 0.005115970969200134, 'train/grad_norm': 0.2569998502731323, 'train/lr_0': 6.220000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 17:53:26] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:53:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:31] train_sft.py:596 - step 4378: {'train/loss': 0.008705973625183105, 'train/grad_norm': 0.21723705530166626, 'train/lr_0': 6.210000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:53:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:53:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:36] train_sft.py:596 - step 4379: {'train/loss': 0.004297852516174316, 'train/grad_norm': 0.21862855553627014, 'train/lr_0': 6.200000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:53:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:53:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:41] train_sft.py:596 - step 4380: {'train/loss': 0.00690431147813797, 'train/grad_norm': 0.21213437616825104, 'train/lr_0': 6.190000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.81s/it]
[2025-12-29 17:53:41] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:53:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:45] train_sft.py:596 - step 4381: {'train/loss': 0.0074922144412994385, 'train/grad_norm': 0.27566489577293396, 'train/lr_0': 6.18e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 17:53:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:53:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:50] train_sft.py:596 - step 4382: {'train/loss': 0.006334953010082245, 'train/grad_norm': 0.29251807928085327, 'train/lr_0': 6.17e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:53:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:53:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:53:55] train_sft.py:596 - step 4383: {'train/loss': 0.007653927430510521, 'train/grad_norm': 0.1689552366733551, 'train/lr_0': 6.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 17:53:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:53:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:00] train_sft.py:596 - step 4384: {'train/loss': 0.008295170962810516, 'train/grad_norm': 0.3376462161540985, 'train/lr_0': 6.15e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:54:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:54:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:05] train_sft.py:596 - step 4385: {'train/loss': 0.005651561543345451, 'train/grad_norm': 0.28945717215538025, 'train/lr_0': 6.1400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:54:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:54:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:09] train_sft.py:596 - step 4386: {'train/loss': 0.004958450794219971, 'train/grad_norm': 0.3445475697517395, 'train/lr_0': 6.130000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:54:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:54:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:14] train_sft.py:596 - step 4387: {'train/loss': 0.006244949996471405, 'train/grad_norm': 0.15761879086494446, 'train/lr_0': 6.120000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:54:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:54:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:19] train_sft.py:596 - step 4388: {'train/loss': 0.007811480667442083, 'train/grad_norm': 0.49871233105659485, 'train/lr_0': 6.110000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:54:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:54:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:23] train_sft.py:596 - step 4389: {'train/loss': 0.012706290930509567, 'train/grad_norm': 0.4744977355003357, 'train/lr_0': 6.1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:54:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:54:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:28] train_sft.py:596 - step 4390: {'train/loss': 0.004424642771482468, 'train/grad_norm': 0.05341050401329994, 'train/lr_0': 6.09e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:54:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:54:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:33] train_sft.py:596 - step 4391: {'train/loss': 0.010810326784849167, 'train/grad_norm': 0.4222487509250641, 'train/lr_0': 6.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:54:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:54:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:37] train_sft.py:596 - step 4392: {'train/loss': 0.007035303860902786, 'train/grad_norm': 0.2978737950325012, 'train/lr_0': 6.07e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 17:54:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:54:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:43] train_sft.py:596 - step 4393: {'train/loss': 0.008218113332986832, 'train/grad_norm': 0.48660463094711304, 'train/lr_0': 6.0600000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 17:54:43] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:54:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:47] train_sft.py:596 - step 4394: {'train/loss': 0.004393015056848526, 'train/grad_norm': 0.3797929883003235, 'train/lr_0': 6.0500000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 17:54:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:54:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:53] train_sft.py:596 - step 4395: {'train/loss': 0.004288738593459129, 'train/grad_norm': 0.16805894672870636, 'train/lr_0': 6.040000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.97s/it]
[2025-12-29 17:54:53] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 17:54:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:54:57] train_sft.py:596 - step 4396: {'train/loss': 0.007115475833415985, 'train/grad_norm': 0.31805527210235596, 'train/lr_0': 6.030000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:54:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:54:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:02] train_sft.py:596 - step 4397: {'train/loss': 0.005525775253772736, 'train/grad_norm': 0.1883336901664734, 'train/lr_0': 6.02e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:55:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:55:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:06] train_sft.py:596 - step 4398: {'train/loss': 0.00832102494314313, 'train/grad_norm': 0.23364576697349548, 'train/lr_0': 6.01e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:55:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:55:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:11] train_sft.py:596 - step 4399: {'train/loss': 0.009348951280117035, 'train/grad_norm': 0.3955811560153961, 'train/lr_0': 6e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 17:55:11] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:55:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:16] train_sft.py:596 - step 4400: {'train/loss': 0.010356813669204712, 'train/grad_norm': 0.3240449130535126, 'train/lr_0': 5.99e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:55:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:55:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:21] train_sft.py:596 - step 4401: {'train/loss': 0.005412435159087181, 'train/grad_norm': 0.11744984984397888, 'train/lr_0': 5.98e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 17:55:21] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:55:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:26] train_sft.py:596 - step 4402: {'train/loss': 0.006074391305446625, 'train/grad_norm': 0.19081343710422516, 'train/lr_0': 5.9700000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:55:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:55:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:31] train_sft.py:596 - step 4403: {'train/loss': 0.0032222196459770203, 'train/grad_norm': 0.17049771547317505, 'train/lr_0': 5.9600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 17:55:31] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:55:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:36] train_sft.py:596 - step 4404: {'train/loss': 0.005205092951655388, 'train/grad_norm': 0.2801985442638397, 'train/lr_0': 5.950000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:55:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:55:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:41] train_sft.py:596 - step 4405: {'train/loss': 0.006426142528653145, 'train/grad_norm': 0.3305381238460541, 'train/lr_0': 5.94e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:55:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:55:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:45] train_sft.py:596 - step 4406: {'train/loss': 0.002194426953792572, 'train/grad_norm': 0.08664388209581375, 'train/lr_0': 5.93e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:55:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:55:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:50] train_sft.py:596 - step 4407: {'train/loss': 0.00490184873342514, 'train/grad_norm': 0.38321438431739807, 'train/lr_0': 5.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:55:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:55:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:54] train_sft.py:596 - step 4408: {'train/loss': 0.003919640555977821, 'train/grad_norm': 0.11678797751665115, 'train/lr_0': 5.91e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:55:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:55:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:55:59] train_sft.py:596 - step 4409: {'train/loss': 0.0057088881731033325, 'train/grad_norm': 0.28135696053504944, 'train/lr_0': 5.900000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 17:55:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:55:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:04] train_sft.py:596 - step 4410: {'train/loss': 0.003618137910962105, 'train/grad_norm': 0.31843459606170654, 'train/lr_0': 5.89e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:56:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:56:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:09] train_sft.py:596 - step 4411: {'train/loss': 0.004087425768375397, 'train/grad_norm': 0.1547616720199585, 'train/lr_0': 5.880000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.88s/it]
[2025-12-29 17:56:09] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:56:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:14] train_sft.py:596 - step 4412: {'train/loss': 0.007479136809706688, 'train/grad_norm': 0.12504439055919647, 'train/lr_0': 5.8700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 17:56:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:56:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:18] train_sft.py:596 - step 4413: {'train/loss': 0.013809490948915482, 'train/grad_norm': 0.6325621604919434, 'train/lr_0': 5.8600000000000015e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:56:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:56:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:23] train_sft.py:596 - step 4414: {'train/loss': 0.010248379781842232, 'train/grad_norm': 0.3983008563518524, 'train/lr_0': 5.85e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:56:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:56:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:28] train_sft.py:596 - step 4415: {'train/loss': 0.009095743298530579, 'train/grad_norm': 0.38436856865882874, 'train/lr_0': 5.840000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:56:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:56:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:33] train_sft.py:596 - step 4416: {'train/loss': 0.004002485424280167, 'train/grad_norm': 0.27181386947631836, 'train/lr_0': 5.83e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 17:56:33] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 17:56:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:38] train_sft.py:596 - step 4417: {'train/loss': 0.005835536867380142, 'train/grad_norm': 0.2799640893936157, 'train/lr_0': 5.820000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:56:38] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:56:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:43] train_sft.py:596 - step 4418: {'train/loss': 0.008611546829342842, 'train/grad_norm': 0.4573664665222168, 'train/lr_0': 5.81e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:56:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:56:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:48] train_sft.py:596 - step 4419: {'train/loss': 0.006865738891065121, 'train/grad_norm': 0.24050584435462952, 'train/lr_0': 5.800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:56:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:56:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:52] train_sft.py:596 - step 4420: {'train/loss': 0.004480317234992981, 'train/grad_norm': 0.1295301914215088, 'train/lr_0': 5.7900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:56:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:56:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:56:57] train_sft.py:596 - step 4421: {'train/loss': 0.005745097994804382, 'train/grad_norm': 0.1668037474155426, 'train/lr_0': 5.780000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 17:56:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:56:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:02] train_sft.py:596 - step 4422: {'train/loss': 0.009759150445461273, 'train/grad_norm': 0.34515905380249023, 'train/lr_0': 5.77e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:57:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:57:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:06] train_sft.py:596 - step 4423: {'train/loss': 0.012124843895435333, 'train/grad_norm': 0.4630196988582611, 'train/lr_0': 5.760000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 17:57:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:57:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:11] train_sft.py:596 - step 4424: {'train/loss': 0.01099894568324089, 'train/grad_norm': 0.3955875635147095, 'train/lr_0': 5.75e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:57:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:57:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:16] train_sft.py:596 - step 4425: {'train/loss': 0.010669166222214699, 'train/grad_norm': 0.39338329434394836, 'train/lr_0': 5.740000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 17:57:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:57:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:21] train_sft.py:596 - step 4426: {'train/loss': 0.00941232405602932, 'train/grad_norm': 0.5813616514205933, 'train/lr_0': 5.73e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:57:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:57:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:25] train_sft.py:596 - step 4427: {'train/loss': 0.011452827602624893, 'train/grad_norm': 0.3262891471385956, 'train/lr_0': 5.720000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 17:57:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:57:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:30] train_sft.py:596 - step 4428: {'train/loss': 0.0035632960498332977, 'train/grad_norm': 0.2882431149482727, 'train/lr_0': 5.71e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 17:57:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:57:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:35] train_sft.py:596 - step 4429: {'train/loss': 0.007380472496151924, 'train/grad_norm': 0.47210416197776794, 'train/lr_0': 5.700000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 17:57:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:57:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:40] train_sft.py:596 - step 4430: {'train/loss': 0.0040936702862381935, 'train/grad_norm': 0.2471185028553009, 'train/lr_0': 5.69e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 17:57:40] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:57:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:45] train_sft.py:596 - step 4431: {'train/loss': 0.017594749107956886, 'train/grad_norm': 0.34789779782295227, 'train/lr_0': 5.680000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 17:57:45] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 17:57:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:49] train_sft.py:596 - step 4432: {'train/loss': 0.019178004935383797, 'train/grad_norm': 0.39144274592399597, 'train/lr_0': 5.67e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:57:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:57:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:54] train_sft.py:596 - step 4433: {'train/loss': 0.005287766456604004, 'train/grad_norm': 0.24399065971374512, 'train/lr_0': 5.660000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 17:57:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:57:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:57:59] train_sft.py:596 - step 4434: {'train/loss': 0.0033789072185754776, 'train/grad_norm': 0.32105308771133423, 'train/lr_0': 5.65e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 17:57:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:57:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:03] train_sft.py:596 - step 4435: {'train/loss': 0.006974346935749054, 'train/grad_norm': 0.3007633090019226, 'train/lr_0': 5.640000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:58:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:58:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:08] train_sft.py:596 - step 4436: {'train/loss': 0.010584890842437744, 'train/grad_norm': 0.3356710970401764, 'train/lr_0': 5.63e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 17:58:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:58:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:13] train_sft.py:596 - step 4437: {'train/loss': 0.006415697745978832, 'train/grad_norm': 0.15421149134635925, 'train/lr_0': 5.620000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:58:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:58:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:18] train_sft.py:596 - step 4438: {'train/loss': 0.0030824244022369385, 'train/grad_norm': 0.21833959221839905, 'train/lr_0': 5.61e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 17:58:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:58:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:22] train_sft.py:596 - step 4439: {'train/loss': 0.006876971572637558, 'train/grad_norm': 0.22580410540103912, 'train/lr_0': 5.600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:58:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:58:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:27] train_sft.py:596 - step 4440: {'train/loss': 0.009310510009527206, 'train/grad_norm': 0.2664412558078766, 'train/lr_0': 5.59e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:58:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:58:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:32] train_sft.py:596 - step 4441: {'train/loss': 0.006130498833954334, 'train/grad_norm': 0.1914198249578476, 'train/lr_0': 5.580000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:58:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:58:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:36] train_sft.py:596 - step 4442: {'train/loss': 0.01311463862657547, 'train/grad_norm': 0.3584345281124115, 'train/lr_0': 5.57e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 17:58:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:58:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:41] train_sft.py:596 - step 4443: {'train/loss': 0.0072319395840168, 'train/grad_norm': 0.43737539649009705, 'train/lr_0': 5.560000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 17:58:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:58:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:45] train_sft.py:596 - step 4444: {'train/loss': 0.005928125232458115, 'train/grad_norm': 0.23153510689735413, 'train/lr_0': 5.55e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:58:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:58:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:50] train_sft.py:596 - step 4445: {'train/loss': 0.006746986880898476, 'train/grad_norm': 0.29486653208732605, 'train/lr_0': 5.540000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 17:58:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:58:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:58:55] train_sft.py:596 - step 4446: {'train/loss': 0.0055871689692139626, 'train/grad_norm': 0.17154568433761597, 'train/lr_0': 5.5299999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:58:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:58:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:00] train_sft.py:596 - step 4447: {'train/loss': 0.010938338935375214, 'train/grad_norm': 0.33694523572921753, 'train/lr_0': 5.5200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 17:59:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 17:59:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:05] train_sft.py:596 - step 4448: {'train/loss': 0.008506210520863533, 'train/grad_norm': 0.2183755785226822, 'train/lr_0': 5.51e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 17:59:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 17:59:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:09] train_sft.py:596 - step 4449: {'train/loss': 0.010332100093364716, 'train/grad_norm': 0.4341154098510742, 'train/lr_0': 5.500000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 17:59:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:59:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:14] train_sft.py:596 - step 4450: {'train/loss': 0.009104873985052109, 'train/grad_norm': 0.4469691514968872, 'train/lr_0': 5.49e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.16s/it]
[2025-12-29 17:59:14] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 17:59:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:19] train_sft.py:596 - step 4451: {'train/loss': 0.005642041563987732, 'train/grad_norm': 0.17351102828979492, 'train/lr_0': 5.480000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 17:59:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:59:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:24] train_sft.py:596 - step 4452: {'train/loss': 0.013279315084218979, 'train/grad_norm': 0.4912397563457489, 'train/lr_0': 5.47e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 17:59:24] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 17:59:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:29] train_sft.py:596 - step 4453: {'train/loss': 0.016548123210668564, 'train/grad_norm': 1.0089856386184692, 'train/lr_0': 5.460000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 17:59:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:59:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:33] train_sft.py:596 - step 4454: {'train/loss': 0.006306305527687073, 'train/grad_norm': 0.18429771065711975, 'train/lr_0': 5.4499999999999995e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 17:59:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:59:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:38] train_sft.py:596 - step 4455: {'train/loss': 0.012192890048027039, 'train/grad_norm': 0.6773744225502014, 'train/lr_0': 5.4400000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 17:59:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:59:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:43] train_sft.py:596 - step 4456: {'train/loss': 0.011530039831995964, 'train/grad_norm': 0.5352675318717957, 'train/lr_0': 5.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:59:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:59:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:47] train_sft.py:596 - step 4457: {'train/loss': 0.007274134084582329, 'train/grad_norm': 0.3539806604385376, 'train/lr_0': 5.420000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 17:59:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:59:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:52] train_sft.py:596 - step 4458: {'train/loss': 0.008065750822424889, 'train/grad_norm': 0.33265236020088196, 'train/lr_0': 5.41e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 17:59:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 17:59:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 17:59:56] train_sft.py:596 - step 4459: {'train/loss': 0.007294919341802597, 'train/grad_norm': 0.33681705594062805, 'train/lr_0': 5.400000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 17:59:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 17:59:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:02] train_sft.py:596 - step 4460: {'train/loss': 0.007937818765640259, 'train/grad_norm': 0.2610245943069458, 'train/lr_0': 5.39e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 18:00:02] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:00:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:06] train_sft.py:596 - step 4461: {'train/loss': 0.01009579747915268, 'train/grad_norm': 0.19884134829044342, 'train/lr_0': 5.380000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:00:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:00:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:11] train_sft.py:596 - step 4462: {'train/loss': 0.00411948561668396, 'train/grad_norm': 0.22805018723011017, 'train/lr_0': 5.369999999999999e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:00:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:00:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:15] train_sft.py:596 - step 4463: {'train/loss': 0.0098038949072361, 'train/grad_norm': 0.32611873745918274, 'train/lr_0': 5.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:00:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:20] train_sft.py:596 - step 4464: {'train/loss': 0.00855616107583046, 'train/grad_norm': 0.13869912922382355, 'train/lr_0': 5.35e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:00:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:25] train_sft.py:596 - step 4465: {'train/loss': 0.0052054934203624725, 'train/grad_norm': 0.3588797152042389, 'train/lr_0': 5.3400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:00:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:29] train_sft.py:596 - step 4466: {'train/loss': 0.00768829882144928, 'train/grad_norm': 0.3873489499092102, 'train/lr_0': 5.33e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:00:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:34] train_sft.py:596 - step 4467: {'train/loss': 0.007668817415833473, 'train/grad_norm': 0.2950868606567383, 'train/lr_0': 5.320000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:00:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:39] train_sft.py:596 - step 4468: {'train/loss': 0.005935288965702057, 'train/grad_norm': 0.20190586149692535, 'train/lr_0': 5.310000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:00:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:43] train_sft.py:596 - step 4469: {'train/loss': 0.01601048745214939, 'train/grad_norm': 0.5244389176368713, 'train/lr_0': 5.300000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:00:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:48] train_sft.py:596 - step 4470: {'train/loss': 0.004799708724021912, 'train/grad_norm': 0.22771811485290527, 'train/lr_0': 5.290000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:00:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:00:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:53] train_sft.py:596 - step 4471: {'train/loss': 0.008330430835485458, 'train/grad_norm': 0.47432875633239746, 'train/lr_0': 5.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:00:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:00:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:00:58] train_sft.py:596 - step 4472: {'train/loss': 0.014710666611790657, 'train/grad_norm': 0.40146055817604065, 'train/lr_0': 5.27e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:00:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:00:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:02] train_sft.py:596 - step 4473: {'train/loss': 0.006663516163825989, 'train/grad_norm': 0.1760087013244629, 'train/lr_0': 5.2600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:01:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:01:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:07] train_sft.py:596 - step 4474: {'train/loss': 0.01000155508518219, 'train/grad_norm': 0.3003896474838257, 'train/lr_0': 5.2500000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:01:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:01:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:12] train_sft.py:596 - step 4475: {'train/loss': 0.011651523411273956, 'train/grad_norm': 0.3981325030326843, 'train/lr_0': 5.240000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:01:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:01:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:17] train_sft.py:596 - step 4476: {'train/loss': 0.007423557341098785, 'train/grad_norm': 0.5223329663276672, 'train/lr_0': 5.230000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:01:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:01:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:22] train_sft.py:596 - step 4477: {'train/loss': 0.004230372607707977, 'train/grad_norm': 0.23464789986610413, 'train/lr_0': 5.220000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:01:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:01:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:26] train_sft.py:596 - step 4478: {'train/loss': 0.004053425043821335, 'train/grad_norm': 0.17444434762001038, 'train/lr_0': 5.210000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:01:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:01:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:31] train_sft.py:596 - step 4479: {'train/loss': 0.008722744882106781, 'train/grad_norm': 0.3307979106903076, 'train/lr_0': 5.2e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:01:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:01:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:36] train_sft.py:596 - step 4480: {'train/loss': 0.004808101803064346, 'train/grad_norm': 0.18488860130310059, 'train/lr_0': 5.19e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:01:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:01:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:40] train_sft.py:596 - step 4481: {'train/loss': 0.008904822170734406, 'train/grad_norm': 0.20856308937072754, 'train/lr_0': 5.18e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 18:01:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:01:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:45] train_sft.py:596 - step 4482: {'train/loss': 0.005311686545610428, 'train/grad_norm': 0.2557617723941803, 'train/lr_0': 5.1700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:01:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:01:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:50] train_sft.py:596 - step 4483: {'train/loss': 0.007953502237796783, 'train/grad_norm': 0.3912736773490906, 'train/lr_0': 5.1600000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:01:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:01:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:55] train_sft.py:596 - step 4484: {'train/loss': 0.007947780191898346, 'train/grad_norm': 0.23717281222343445, 'train/lr_0': 5.150000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 18:01:55] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:01:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:01:59] train_sft.py:596 - step 4485: {'train/loss': 0.006474234163761139, 'train/grad_norm': 0.39719870686531067, 'train/lr_0': 5.140000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 18:01:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:02:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:04] train_sft.py:596 - step 4486: {'train/loss': 0.010808590799570084, 'train/grad_norm': 0.29748716950416565, 'train/lr_0': 5.130000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:02:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:02:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:09] train_sft.py:596 - step 4487: {'train/loss': 0.007651768624782562, 'train/grad_norm': 0.2942976653575897, 'train/lr_0': 5.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.77s/it]
[2025-12-29 18:02:09] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:02:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:14] train_sft.py:596 - step 4488: {'train/loss': 0.007764503359794617, 'train/grad_norm': 0.22191685438156128, 'train/lr_0': 5.11e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:02:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:02:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:19] train_sft.py:596 - step 4489: {'train/loss': 0.005880339071154594, 'train/grad_norm': 0.17533838748931885, 'train/lr_0': 5.1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:02:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:02:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:23] train_sft.py:596 - step 4490: {'train/loss': 0.0036294423043727875, 'train/grad_norm': 0.09750276058912277, 'train/lr_0': 5.09e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:02:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:02:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:28] train_sft.py:596 - step 4491: {'train/loss': 0.012104952707886696, 'train/grad_norm': 0.8929824829101562, 'train/lr_0': 5.0800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:02:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:02:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:32] train_sft.py:596 - step 4492: {'train/loss': 0.004999324679374695, 'train/grad_norm': 0.17465706169605255, 'train/lr_0': 5.070000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:02:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:02:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:37] train_sft.py:596 - step 4493: {'train/loss': 0.00394989550113678, 'train/grad_norm': 0.12015222758054733, 'train/lr_0': 5.060000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:02:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:02:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:42] train_sft.py:596 - step 4494: {'train/loss': 0.005898360162973404, 'train/grad_norm': 0.1699572205543518, 'train/lr_0': 5.050000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:02:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:02:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:47] train_sft.py:596 - step 4495: {'train/loss': 0.008944060653448105, 'train/grad_norm': 0.22119757533073425, 'train/lr_0': 5.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 18:02:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:02:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:51] train_sft.py:596 - step 4496: {'train/loss': 0.006396204233169556, 'train/grad_norm': 0.2004106640815735, 'train/lr_0': 5.03e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:02:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:02:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:02:56] train_sft.py:596 - step 4497: {'train/loss': 0.0082129817456007, 'train/grad_norm': 0.2565965950489044, 'train/lr_0': 5.02e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:02:56] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:02:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:03:01] train_sft.py:596 - step 4498: {'train/loss': 0.006604515016078949, 'train/grad_norm': 0.21713386476039886, 'train/lr_0': 5.01e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:03:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:03:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:03:06] train_sft.py:596 - step 4499: {'train/loss': 0.01195606216788292, 'train/grad_norm': 0.41057395935058594, 'train/lr_0': 5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:03:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:01<02:48,  1.70s/it]
actor_val:   2%|▏         | 2/100 [00:03<02:50,  1.74s/it]
actor_val:   3%|▎         | 3/100 [00:05<02:40,  1.65s/it]
actor_val:   4%|▍         | 4/100 [00:06<02:46,  1.74s/it]
actor_val:   5%|▌         | 5/100 [00:08<02:50,  1.79s/it]
actor_val:   6%|▌         | 6/100 [00:10<02:41,  1.72s/it]
actor_val:   7%|▋         | 7/100 [00:11<02:33,  1.65s/it]
actor_val:   8%|▊         | 8/100 [00:13<02:28,  1.62s/it]
actor_val:   9%|▉         | 9/100 [00:14<02:25,  1.60s/it]
actor_val:  10%|█         | 10/100 [00:16<02:22,  1.58s/it]
actor_val:  11%|█         | 11/100 [00:18<02:20,  1.58s/it]
actor_val:  12%|█▏        | 12/100 [00:19<02:24,  1.64s/it]
actor_val:  13%|█▎        | 13/100 [00:21<02:20,  1.61s/it]
actor_val:  14%|█▍        | 14/100 [00:22<02:17,  1.60s/it]
actor_val:  15%|█▌        | 15/100 [00:24<02:14,  1.58s/it]
actor_val:  16%|█▌        | 16/100 [00:26<02:10,  1.55s/it]
actor_val:  17%|█▋        | 17/100 [00:27<02:16,  1.64s/it]
actor_val:  18%|█▊        | 18/100 [00:29<02:12,  1.62s/it]
actor_val:  19%|█▉        | 19/100 [00:30<02:09,  1.59s/it]
actor_val:  20%|██        | 20/100 [00:32<02:06,  1.58s/it]
actor_val:  21%|██        | 21/100 [00:34<02:04,  1.57s/it]
actor_val:  22%|██▏       | 22/100 [00:35<02:02,  1.57s/it]
actor_val:  23%|██▎       | 23/100 [00:37<02:06,  1.64s/it]
actor_val:  24%|██▍       | 24/100 [00:38<02:02,  1.61s/it]
actor_val:  25%|██▌       | 25/100 [00:40<01:58,  1.59s/it]
actor_val:  26%|██▌       | 26/100 [00:42<01:56,  1.58s/it]
actor_val:  27%|██▋       | 27/100 [00:43<01:54,  1.57s/it]
actor_val:  28%|██▊       | 28/100 [00:45<01:52,  1.57s/it]
actor_val:  29%|██▉       | 29/100 [00:46<01:50,  1.56s/it]
actor_val:  30%|███       | 30/100 [00:48<01:53,  1.63s/it]
actor_val:  31%|███       | 31/100 [00:50<01:55,  1.68s/it]
actor_val:  32%|███▏      | 32/100 [00:52<01:56,  1.72s/it]
actor_val:  33%|███▎      | 33/100 [00:53<01:51,  1.66s/it]
actor_val:  34%|███▍      | 34/100 [00:55<01:52,  1.70s/it]
actor_val:  35%|███▌      | 35/100 [00:56<01:47,  1.65s/it]
actor_val:  36%|███▌      | 36/100 [00:58<01:48,  1.70s/it]
actor_val:  37%|███▋      | 37/100 [01:00<01:44,  1.66s/it]
actor_val:  38%|███▊      | 38/100 [01:01<01:40,  1.62s/it]
actor_val:  39%|███▉      | 39/100 [01:03<01:42,  1.67s/it]
actor_val:  40%|████      | 40/100 [01:05<01:37,  1.63s/it]
actor_val:  41%|████      | 41/100 [01:06<01:35,  1.61s/it]
actor_val:  42%|████▏     | 42/100 [01:08<01:31,  1.58s/it]
actor_val:  43%|████▎     | 43/100 [01:10<01:34,  1.66s/it]
actor_val:  44%|████▍     | 44/100 [01:11<01:30,  1.61s/it]
actor_val:  45%|████▌     | 45/100 [01:13<01:31,  1.66s/it]
actor_val:  46%|████▌     | 46/100 [01:14<01:27,  1.63s/it]
actor_val:  47%|████▋     | 47/100 [01:16<01:28,  1.68s/it]
actor_val:  48%|████▊     | 48/100 [01:18<01:24,  1.63s/it]
actor_val:  49%|████▉     | 49/100 [01:19<01:22,  1.61s/it]
actor_val:  50%|█████     | 50/100 [01:21<01:19,  1.59s/it]
actor_val:  51%|█████     | 51/100 [01:22<01:17,  1.58s/it]
actor_val:  52%|█████▏    | 52/100 [01:24<01:19,  1.66s/it]
actor_val:  53%|█████▎    | 53/100 [01:26<01:16,  1.63s/it]
actor_val:  54%|█████▍    | 54/100 [01:27<01:13,  1.60s/it]
actor_val:  55%|█████▌    | 55/100 [01:29<01:14,  1.66s/it]
actor_val:  56%|█████▌    | 56/100 [01:31<01:11,  1.63s/it]
actor_val:  57%|█████▋    | 57/100 [01:33<01:12,  1.69s/it]
actor_val:  58%|█████▊    | 58/100 [01:34<01:11,  1.71s/it]
actor_val:  59%|█████▉    | 59/100 [01:36<01:07,  1.65s/it]
actor_val:  60%|██████    | 60/100 [01:37<01:04,  1.61s/it]
actor_val:  61%|██████    | 61/100 [01:39<01:02,  1.60s/it]
actor_val:  62%|██████▏   | 62/100 [01:40<01:00,  1.58s/it]
actor_val:  63%|██████▎   | 63/100 [01:42<00:57,  1.57s/it]
actor_val:  64%|██████▍   | 64/100 [01:43<00:56,  1.56s/it]
actor_val:  65%|██████▌   | 65/100 [01:45<00:54,  1.56s/it]
actor_val:  66%|██████▌   | 66/100 [01:47<00:52,  1.55s/it]
actor_val:  67%|██████▋   | 67/100 [01:48<00:54,  1.65s/it]
actor_val:  68%|██████▊   | 68/100 [01:50<00:51,  1.61s/it]
actor_val:  69%|██████▉   | 69/100 [01:52<00:49,  1.60s/it]
actor_val:  70%|███████   | 70/100 [01:53<00:47,  1.57s/it]
actor_val:  71%|███████   | 71/100 [01:55<00:47,  1.64s/it]
actor_val:  72%|███████▏  | 72/100 [01:57<00:47,  1.71s/it]
actor_val:  73%|███████▎  | 73/100 [01:58<00:46,  1.72s/it]
actor_val:  74%|███████▍  | 74/100 [02:00<00:43,  1.67s/it]
actor_val:  75%|███████▌  | 75/100 [02:02<00:42,  1.70s/it]
actor_val:  76%|███████▌  | 76/100 [02:03<00:39,  1.65s/it]
actor_val:  77%|███████▋  | 77/100 [02:05<00:36,  1.60s/it]
actor_val:  78%|███████▊  | 78/100 [02:07<00:36,  1.67s/it]
actor_val:  79%|███████▉  | 79/100 [02:08<00:34,  1.63s/it]
actor_val:  80%|████████  | 80/100 [02:10<00:31,  1.59s/it]
actor_val:  81%|████████  | 81/100 [02:11<00:30,  1.58s/it]
actor_val:  82%|████████▏ | 82/100 [02:13<00:29,  1.65s/it]
actor_val:  83%|████████▎ | 83/100 [02:15<00:27,  1.62s/it]
actor_val:  84%|████████▍ | 84/100 [02:16<00:26,  1.66s/it]
actor_val:  85%|████████▌ | 85/100 [02:18<00:24,  1.64s/it]
actor_val:  86%|████████▌ | 86/100 [02:19<00:22,  1.61s/it]
actor_val:  87%|████████▋ | 87/100 [02:21<00:20,  1.58s/it]
actor_val:  88%|████████▊ | 88/100 [02:23<00:18,  1.57s/it]
actor_val:  89%|████████▉ | 89/100 [02:24<00:18,  1.67s/it]
actor_val:  90%|█████████ | 90/100 [02:26<00:17,  1.71s/it]
actor_val:  91%|█████████ | 91/100 [02:28<00:15,  1.74s/it]
actor_val:  92%|█████████▏| 92/100 [02:30<00:14,  1.76s/it]
actor_val:  93%|█████████▎| 93/100 [02:32<00:12,  1.79s/it]
actor_val:  94%|█████████▍| 94/100 [02:33<00:10,  1.72s/it]
actor_val:  95%|█████████▌| 95/100 [02:35<00:08,  1.74s/it]
actor_val:  96%|█████████▌| 96/100 [02:37<00:06,  1.68s/it]
actor_val:  97%|█████████▋| 97/100 [02:38<00:04,  1.63s/it]
actor_val:  98%|█████████▊| 98/100 [02:40<00:03,  1.61s/it]
actor_val:  99%|█████████▉| 99/100 [02:42<00:01,  1.67s/it]
actor_val: 100%|██████████| 100/100 [02:43<00:00,  1.64s/it]
actor_val: 100%|██████████| 100/100 [02:43<00:00,  1.64s/it]
[2025-12-29 18:05:49] train_sft.py:658 - step 4500: {'val/loss': 0.498359375}
[2025-12-29 18:05:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:05:54] train_sft.py:596 - step 4500: {'train/loss': 0.006905080284923315, 'train/grad_norm': 0.3898661434650421, 'train/lr_0': 4.9900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.49s/it]
[2025-12-29 18:05:54] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:05:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:05:59] train_sft.py:596 - step 4501: {'train/loss': 0.004982542246580124, 'train/grad_norm': 0.1931501030921936, 'train/lr_0': 4.980000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 18:05:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:05:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:04] train_sft.py:596 - step 4502: {'train/loss': 0.0057892948389053345, 'train/grad_norm': 0.1724442094564438, 'train/lr_0': 4.970000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 18:06:04] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:06:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:09] train_sft.py:596 - step 4503: {'train/loss': 0.008935045450925827, 'train/grad_norm': 0.3410157263278961, 'train/lr_0': 4.960000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:06:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:06:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:13] train_sft.py:596 - step 4504: {'train/loss': 0.015137378126382828, 'train/grad_norm': 0.4088374376296997, 'train/lr_0': 4.95e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:06:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:06:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:18] train_sft.py:596 - step 4505: {'train/loss': 0.002521997317671776, 'train/grad_norm': 0.15588180720806122, 'train/lr_0': 4.94e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 18:06:18] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:06:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:23] train_sft.py:596 - step 4506: {'train/loss': 0.0041353739798069, 'train/grad_norm': 0.20365378260612488, 'train/lr_0': 4.93e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:06:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:06:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:28] train_sft.py:596 - step 4507: {'train/loss': 0.008704867213964462, 'train/grad_norm': 0.24684248864650726, 'train/lr_0': 4.92e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:06:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:06:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:32] train_sft.py:596 - step 4508: {'train/loss': 0.005507428199052811, 'train/grad_norm': 0.19392235577106476, 'train/lr_0': 4.9100000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:06:32] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:06:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:37] train_sft.py:596 - step 4509: {'train/loss': 0.00949881598353386, 'train/grad_norm': 0.19617860019207, 'train/lr_0': 4.9000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 18:06:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:06:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:42] train_sft.py:596 - step 4510: {'train/loss': 0.006598389241844416, 'train/grad_norm': 0.28986087441444397, 'train/lr_0': 4.890000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 18:06:42] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:06:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:47] train_sft.py:596 - step 4511: {'train/loss': 0.005635792389512062, 'train/grad_norm': 0.15887345373630524, 'train/lr_0': 4.880000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:06:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:06:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:52] train_sft.py:596 - step 4512: {'train/loss': 0.004544809460639954, 'train/grad_norm': 0.2160143107175827, 'train/lr_0': 4.87e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.78s/it]
[2025-12-29 18:06:52] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:06:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:06:57] train_sft.py:596 - step 4513: {'train/loss': 0.008569864556193352, 'train/grad_norm': 0.29862484335899353, 'train/lr_0': 4.86e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:06:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:06:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:01] train_sft.py:596 - step 4514: {'train/loss': 0.007326532155275345, 'train/grad_norm': 0.3195679485797882, 'train/lr_0': 4.85e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:07:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:07:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:06] train_sft.py:596 - step 4515: {'train/loss': 0.005032002925872803, 'train/grad_norm': 0.12278012186288834, 'train/lr_0': 4.84e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:07:06] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:07:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:11] train_sft.py:596 - step 4516: {'train/loss': 0.008118122816085815, 'train/grad_norm': 0.4260123670101166, 'train/lr_0': 4.83e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:07:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:07:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:15] train_sft.py:596 - step 4517: {'train/loss': 0.01000446081161499, 'train/grad_norm': 0.376924067735672, 'train/lr_0': 4.8200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:07:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:07:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:20] train_sft.py:596 - step 4518: {'train/loss': 0.008296100422739983, 'train/grad_norm': 0.21625152230262756, 'train/lr_0': 4.8100000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:07:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:07:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:25] train_sft.py:596 - step 4519: {'train/loss': 0.005067937076091766, 'train/grad_norm': 0.32849326729774475, 'train/lr_0': 4.800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:07:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:07:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:29] train_sft.py:596 - step 4520: {'train/loss': 0.006676182150840759, 'train/grad_norm': 0.3811884820461273, 'train/lr_0': 4.79e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:07:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:07:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:34] train_sft.py:596 - step 4521: {'train/loss': 0.009465910494327545, 'train/grad_norm': 0.5506256222724915, 'train/lr_0': 4.78e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:07:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:07:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:39] train_sft.py:596 - step 4522: {'train/loss': 0.005464449524879456, 'train/grad_norm': 0.33000028133392334, 'train/lr_0': 4.77e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:07:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:07:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:43] train_sft.py:596 - step 4523: {'train/loss': 0.008542470633983612, 'train/grad_norm': 0.3882162570953369, 'train/lr_0': 4.76e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 18:07:43] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:07:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:48] train_sft.py:596 - step 4524: {'train/loss': 0.007056213915348053, 'train/grad_norm': 0.3142428398132324, 'train/lr_0': 4.75e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:07:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:07:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:53] train_sft.py:596 - step 4525: {'train/loss': 0.00933303963392973, 'train/grad_norm': 0.5312061309814453, 'train/lr_0': 4.74e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 18:07:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:07:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:07:58] train_sft.py:596 - step 4526: {'train/loss': 0.010440859943628311, 'train/grad_norm': 0.43210887908935547, 'train/lr_0': 4.7300000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:07:58] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:07:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:02] train_sft.py:596 - step 4527: {'train/loss': 0.0089632049202919, 'train/grad_norm': 0.38713160157203674, 'train/lr_0': 4.7200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:08:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:08:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:08] train_sft.py:596 - step 4528: {'train/loss': 0.006725854240357876, 'train/grad_norm': 0.22571273148059845, 'train/lr_0': 4.71e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 18:08:08] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:08:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:12] train_sft.py:596 - step 4529: {'train/loss': 0.005098212510347366, 'train/grad_norm': 0.22294770181179047, 'train/lr_0': 4.7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:08:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:08:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:17] train_sft.py:596 - step 4530: {'train/loss': 0.005096521228551865, 'train/grad_norm': 0.2622024416923523, 'train/lr_0': 4.69e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.83s/it]
[2025-12-29 18:08:17] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:08:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:22] train_sft.py:596 - step 4531: {'train/loss': 0.004284543916583061, 'train/grad_norm': 0.42793214321136475, 'train/lr_0': 4.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:08:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:08:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:27] train_sft.py:596 - step 4532: {'train/loss': 0.00670977309346199, 'train/grad_norm': 0.4315287172794342, 'train/lr_0': 4.67e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:08:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:08:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:32] train_sft.py:596 - step 4533: {'train/loss': 0.00443362258374691, 'train/grad_norm': 0.19383695721626282, 'train/lr_0': 4.66e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:08:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:08:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:36] train_sft.py:596 - step 4534: {'train/loss': 0.003938509151339531, 'train/grad_norm': 0.20553375780582428, 'train/lr_0': 4.65e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:08:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:08:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:41] train_sft.py:596 - step 4535: {'train/loss': 0.00445246696472168, 'train/grad_norm': 0.2718524634838104, 'train/lr_0': 4.6400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:08:41] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:08:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:46] train_sft.py:596 - step 4536: {'train/loss': 0.006754156202077866, 'train/grad_norm': 0.2860308885574341, 'train/lr_0': 4.63e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 18:08:46] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:08:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:51] train_sft.py:596 - step 4537: {'train/loss': 0.00531370472162962, 'train/grad_norm': 0.33952611684799194, 'train/lr_0': 4.62e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 18:08:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:08:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:08:56] train_sft.py:596 - step 4538: {'train/loss': 0.00421791709959507, 'train/grad_norm': 0.2382318526506424, 'train/lr_0': 4.61e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:08:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:08:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:01] train_sft.py:596 - step 4539: {'train/loss': 0.004567197524011135, 'train/grad_norm': 0.1938478797674179, 'train/lr_0': 4.6e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 18:09:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:09:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:05] train_sft.py:596 - step 4540: {'train/loss': 0.004932275041937828, 'train/grad_norm': 0.2097884714603424, 'train/lr_0': 4.59e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:09:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:09:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:11] train_sft.py:596 - step 4541: {'train/loss': 0.005793564021587372, 'train/grad_norm': 0.14131887257099152, 'train/lr_0': 4.58e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 18:09:11] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:09:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:15] train_sft.py:596 - step 4542: {'train/loss': 0.006093943491578102, 'train/grad_norm': 0.3896925151348114, 'train/lr_0': 4.57e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:09:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:09:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:20] train_sft.py:596 - step 4543: {'train/loss': 0.0037743085995316505, 'train/grad_norm': 0.21735341846942902, 'train/lr_0': 4.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:09:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:09:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:25] train_sft.py:596 - step 4544: {'train/loss': 0.002983707934617996, 'train/grad_norm': 0.05687344819307327, 'train/lr_0': 4.55e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:09:25] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:09:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:29] train_sft.py:596 - step 4545: {'train/loss': 0.009229173883795738, 'train/grad_norm': 0.4287581741809845, 'train/lr_0': 4.54e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:09:29] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:09:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:34] train_sft.py:596 - step 4546: {'train/loss': 0.002639463171362877, 'train/grad_norm': 0.06018677353858948, 'train/lr_0': 4.53e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:09:34] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:09:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:40] train_sft.py:596 - step 4547: {'train/loss': 0.0038627535104751587, 'train/grad_norm': 0.19174842536449432, 'train/lr_0': 4.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:09:40] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:09:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:44] train_sft.py:596 - step 4548: {'train/loss': 0.004584267735481262, 'train/grad_norm': 0.16934914886951447, 'train/lr_0': 4.51e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:09:44] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:09:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:49] train_sft.py:596 - step 4549: {'train/loss': 0.004539273679256439, 'train/grad_norm': 0.15397599339485168, 'train/lr_0': 4.5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:09:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:09:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:54] train_sft.py:596 - step 4550: {'train/loss': 0.009340953081846237, 'train/grad_norm': 0.20787203311920166, 'train/lr_0': 4.49e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 18:09:54] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:09:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:09:58] train_sft.py:596 - step 4551: {'train/loss': 0.006364105269312859, 'train/grad_norm': 0.24866746366024017, 'train/lr_0': 4.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:09:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:09:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:03] train_sft.py:596 - step 4552: {'train/loss': 0.029718655161559582, 'train/grad_norm': 0.7419675588607788, 'train/lr_0': 4.4699999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:10:03] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:10:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:08] train_sft.py:596 - step 4553: {'train/loss': 0.005123848095536232, 'train/grad_norm': 0.13483083248138428, 'train/lr_0': 4.46e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:10:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:10:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:13] train_sft.py:596 - step 4554: {'train/loss': 0.006044703535735607, 'train/grad_norm': 0.22173656523227692, 'train/lr_0': 4.45e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:10:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:10:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:17] train_sft.py:596 - step 4555: {'train/loss': 0.007454779930412769, 'train/grad_norm': 0.13520154356956482, 'train/lr_0': 4.44e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:10:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:10:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:22] train_sft.py:596 - step 4556: {'train/loss': 0.0037570297718048096, 'train/grad_norm': 0.2953159511089325, 'train/lr_0': 4.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:10:22] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:10:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:27] train_sft.py:596 - step 4557: {'train/loss': 0.011189369484782219, 'train/grad_norm': 0.21287967264652252, 'train/lr_0': 4.42e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:10:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:10:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:31] train_sft.py:596 - step 4558: {'train/loss': 0.005599696189165115, 'train/grad_norm': 0.42554226517677307, 'train/lr_0': 4.41e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:10:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:10:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:36] train_sft.py:596 - step 4559: {'train/loss': 0.006249604746699333, 'train/grad_norm': 0.33588889241218567, 'train/lr_0': 4.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 18:10:36] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:10:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:41] train_sft.py:596 - step 4560: {'train/loss': 0.004058070480823517, 'train/grad_norm': 0.17228807508945465, 'train/lr_0': 4.3899999999999995e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:10:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:10:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:45] train_sft.py:596 - step 4561: {'train/loss': 0.005133398808538914, 'train/grad_norm': 0.15384861826896667, 'train/lr_0': 4.3799999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 18:10:45] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:10:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:50] train_sft.py:596 - step 4562: {'train/loss': 0.010316327214241028, 'train/grad_norm': 0.4696752429008484, 'train/lr_0': 4.3700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:10:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:10:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:10:55] train_sft.py:596 - step 4563: {'train/loss': 0.003910752944648266, 'train/grad_norm': 0.05735950171947479, 'train/lr_0': 4.360000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:10:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:10:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:00] train_sft.py:596 - step 4564: {'train/loss': 0.007116343360394239, 'train/grad_norm': 0.47203341126441956, 'train/lr_0': 4.350000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:11:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:11:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:04] train_sft.py:596 - step 4565: {'train/loss': 0.0040879882872104645, 'train/grad_norm': 0.07448246330022812, 'train/lr_0': 4.340000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 18:11:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:11:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:09] train_sft.py:596 - step 4566: {'train/loss': 0.0043652234598994255, 'train/grad_norm': 0.19338761270046234, 'train/lr_0': 4.330000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:11:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:11:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:14] train_sft.py:596 - step 4567: {'train/loss': 0.002968910150229931, 'train/grad_norm': 0.18254460394382477, 'train/lr_0': 4.320000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:11:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:11:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:19] train_sft.py:596 - step 4568: {'train/loss': 0.006872204132378101, 'train/grad_norm': 0.2057717889547348, 'train/lr_0': 4.310000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 18:11:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:11:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:23] train_sft.py:596 - step 4569: {'train/loss': 0.009404247161000967, 'train/grad_norm': 0.3976760506629944, 'train/lr_0': 4.300000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:11:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:11:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:28] train_sft.py:596 - step 4570: {'train/loss': 0.0035380031913518906, 'train/grad_norm': 0.13401710987091064, 'train/lr_0': 4.2900000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:11:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:11:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:32] train_sft.py:596 - step 4571: {'train/loss': 0.0037224777042865753, 'train/grad_norm': 0.16444580256938934, 'train/lr_0': 4.2800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:11:32] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:11:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:37] train_sft.py:596 - step 4572: {'train/loss': 0.0044261738657951355, 'train/grad_norm': 0.2637796700000763, 'train/lr_0': 4.270000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 18:11:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:11:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:42] train_sft.py:596 - step 4573: {'train/loss': 0.005066988989710808, 'train/grad_norm': 0.18758542835712433, 'train/lr_0': 4.260000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:11:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:11:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:47] train_sft.py:596 - step 4574: {'train/loss': 0.0042716097086668015, 'train/grad_norm': 0.19723278284072876, 'train/lr_0': 4.250000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 18:11:47] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:11:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:51] train_sft.py:596 - step 4575: {'train/loss': 0.0044172052294015884, 'train/grad_norm': 0.2512544095516205, 'train/lr_0': 4.240000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:11:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:11:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:11:56] train_sft.py:596 - step 4576: {'train/loss': 0.03981366753578186, 'train/grad_norm': 1.0369644165039062, 'train/lr_0': 4.230000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 18:11:56] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:11:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:01] train_sft.py:596 - step 4577: {'train/loss': 0.002513062208890915, 'train/grad_norm': 0.2618563771247864, 'train/lr_0': 4.220000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:12:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:12:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:05] train_sft.py:596 - step 4578: {'train/loss': 0.0062210336327552795, 'train/grad_norm': 0.3568287491798401, 'train/lr_0': 4.21e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:12:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:12:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:10] train_sft.py:596 - step 4579: {'train/loss': 0.005335137248039246, 'train/grad_norm': 0.1979939043521881, 'train/lr_0': 4.2000000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 18:12:10] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:12:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:14] train_sft.py:596 - step 4580: {'train/loss': 0.0017335377633571625, 'train/grad_norm': 0.04953131824731827, 'train/lr_0': 4.1900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:12:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:12:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:19] train_sft.py:596 - step 4581: {'train/loss': 0.007510762196034193, 'train/grad_norm': 0.2771926820278168, 'train/lr_0': 4.180000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 18:12:19] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:12:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:24] train_sft.py:596 - step 4582: {'train/loss': 0.002913879230618477, 'train/grad_norm': 0.10874392092227936, 'train/lr_0': 4.170000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:12:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:12:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:29] train_sft.py:596 - step 4583: {'train/loss': 0.0014092996716499329, 'train/grad_norm': 0.08132939785718918, 'train/lr_0': 4.160000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.45s/it]
[2025-12-29 18:12:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:12:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:34] train_sft.py:596 - step 4584: {'train/loss': 0.008621657267212868, 'train/grad_norm': 0.4276869595050812, 'train/lr_0': 4.150000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:12:34] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:12:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:38] train_sft.py:596 - step 4585: {'train/loss': 0.01246441900730133, 'train/grad_norm': 0.5385201573371887, 'train/lr_0': 4.140000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 18:12:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:12:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:43] train_sft.py:596 - step 4586: {'train/loss': 0.0032000597566366196, 'train/grad_norm': 0.12433542311191559, 'train/lr_0': 4.13e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 18:12:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:12:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:47] train_sft.py:596 - step 4587: {'train/loss': 0.0053139179944992065, 'train/grad_norm': 0.31286945939064026, 'train/lr_0': 4.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:12:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:12:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:52] train_sft.py:596 - step 4588: {'train/loss': 0.004562532063573599, 'train/grad_norm': 0.16885210573673248, 'train/lr_0': 4.1100000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:12:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:12:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:12:57] train_sft.py:596 - step 4589: {'train/loss': 0.006148362532258034, 'train/grad_norm': 0.40688854455947876, 'train/lr_0': 4.1000000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:12:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:12:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:01] train_sft.py:596 - step 4590: {'train/loss': 0.003093292936682701, 'train/grad_norm': 0.147359699010849, 'train/lr_0': 4.090000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:13:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:13:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:06] train_sft.py:596 - step 4591: {'train/loss': 0.005375145003199577, 'train/grad_norm': 0.1591797024011612, 'train/lr_0': 4.080000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:13:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:13:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:11] train_sft.py:596 - step 4592: {'train/loss': 0.004690971225500107, 'train/grad_norm': 0.17454273998737335, 'train/lr_0': 4.070000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 18:13:11] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:13:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:16] train_sft.py:596 - step 4593: {'train/loss': 0.004307299852371216, 'train/grad_norm': 0.10416175425052643, 'train/lr_0': 4.060000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:13:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:13:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:21] train_sft.py:596 - step 4594: {'train/loss': 0.0016340464353561401, 'train/grad_norm': 0.03321350738406181, 'train/lr_0': 4.05e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:13:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:13:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:25] train_sft.py:596 - step 4595: {'train/loss': 0.003021063283085823, 'train/grad_norm': 0.11821109056472778, 'train/lr_0': 4.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.48s/it]
[2025-12-29 18:13:25] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:13:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:31] train_sft.py:596 - step 4596: {'train/loss': 0.002315199002623558, 'train/grad_norm': 0.12782135605812073, 'train/lr_0': 4.03e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 18:13:31] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:13:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:35] train_sft.py:596 - step 4597: {'train/loss': 0.0036471690982580185, 'train/grad_norm': 0.1855524629354477, 'train/lr_0': 4.0200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:13:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:13:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:40] train_sft.py:596 - step 4598: {'train/loss': 0.0032753366976976395, 'train/grad_norm': 0.23381683230400085, 'train/lr_0': 4.0100000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 18:13:40] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:13:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:45] train_sft.py:596 - step 4599: {'train/loss': 0.004054125398397446, 'train/grad_norm': 0.2009703516960144, 'train/lr_0': 4.000000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:13:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:13:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:49] train_sft.py:596 - step 4600: {'train/loss': 0.005196042358875275, 'train/grad_norm': 0.3266843557357788, 'train/lr_0': 3.990000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:13:49] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:13:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:54] train_sft.py:596 - step 4601: {'train/loss': 0.004722617566585541, 'train/grad_norm': 0.19984228909015656, 'train/lr_0': 3.980000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:13:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:13:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:13:59] train_sft.py:596 - step 4602: {'train/loss': 0.007571834605187178, 'train/grad_norm': 0.22691921889781952, 'train/lr_0': 3.97e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:13:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:13:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:03] train_sft.py:596 - step 4603: {'train/loss': 0.007158270105719566, 'train/grad_norm': 0.3096412420272827, 'train/lr_0': 3.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:14:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:14:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:08] train_sft.py:596 - step 4604: {'train/loss': 0.003965324722230434, 'train/grad_norm': 0.09111891686916351, 'train/lr_0': 3.95e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:14:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:14:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:13] train_sft.py:596 - step 4605: {'train/loss': 0.004053963348269463, 'train/grad_norm': 0.11381334811449051, 'train/lr_0': 3.94e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:14:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:14:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:18] train_sft.py:596 - step 4606: {'train/loss': 0.005034353584051132, 'train/grad_norm': 0.24189390242099762, 'train/lr_0': 3.9300000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:14:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:14:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:22] train_sft.py:596 - step 4607: {'train/loss': 0.006084125488996506, 'train/grad_norm': 0.2037375420331955, 'train/lr_0': 3.920000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:14:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:14:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:27] train_sft.py:596 - step 4608: {'train/loss': 0.011489166878163815, 'train/grad_norm': 0.5465229153633118, 'train/lr_0': 3.910000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:14:27] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:14:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:32] train_sft.py:596 - step 4609: {'train/loss': 0.006782963406294584, 'train/grad_norm': 0.27689409255981445, 'train/lr_0': 3.900000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 18:14:32] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:14:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:37] train_sft.py:596 - step 4610: {'train/loss': 0.005714816972613335, 'train/grad_norm': 0.20872615277767181, 'train/lr_0': 3.89e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:14:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:14:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:41] train_sft.py:596 - step 4611: {'train/loss': 0.009118951857089996, 'train/grad_norm': 0.5556774139404297, 'train/lr_0': 3.88e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:14:41] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:14:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:46] train_sft.py:596 - step 4612: {'train/loss': 0.007422592490911484, 'train/grad_norm': 0.4083300530910492, 'train/lr_0': 3.87e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:14:46] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:14:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:51] train_sft.py:596 - step 4613: {'train/loss': 0.007613908499479294, 'train/grad_norm': 0.2803317904472351, 'train/lr_0': 3.86e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:14:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:14:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:14:55] train_sft.py:596 - step 4614: {'train/loss': 0.006303383037447929, 'train/grad_norm': 0.3051859736442566, 'train/lr_0': 3.85e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:14:55] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:14:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:00] train_sft.py:596 - step 4615: {'train/loss': 0.009469553828239441, 'train/grad_norm': 0.17254769802093506, 'train/lr_0': 3.8400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:15:00] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:15:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:05] train_sft.py:596 - step 4616: {'train/loss': 0.006745325401425362, 'train/grad_norm': 0.35986027121543884, 'train/lr_0': 3.830000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:15:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:15:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:10] train_sft.py:596 - step 4617: {'train/loss': 0.008900827495381236, 'train/grad_norm': 0.3574245572090149, 'train/lr_0': 3.820000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:15:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:15:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:15] train_sft.py:596 - step 4618: {'train/loss': 0.006135254632681608, 'train/grad_norm': 0.3021659255027771, 'train/lr_0': 3.8100000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:15:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:15:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:20] train_sft.py:596 - step 4619: {'train/loss': 0.0034408271312713623, 'train/grad_norm': 0.14344574511051178, 'train/lr_0': 3.8000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.80s/it]
[2025-12-29 18:15:20] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:15:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:24] train_sft.py:596 - step 4620: {'train/loss': 0.0082462839782238, 'train/grad_norm': 0.4748624265193939, 'train/lr_0': 3.79e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:15:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:15:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:29] train_sft.py:596 - step 4621: {'train/loss': 0.004386173561215401, 'train/grad_norm': 0.18140076100826263, 'train/lr_0': 3.7800000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:15:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:15:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:34] train_sft.py:596 - step 4622: {'train/loss': 0.006311783567070961, 'train/grad_norm': 0.22506669163703918, 'train/lr_0': 3.7700000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.75s/it]
[2025-12-29 18:15:34] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:15:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:39] train_sft.py:596 - step 4623: {'train/loss': 0.004275945946574211, 'train/grad_norm': 0.2529686987400055, 'train/lr_0': 3.7600000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:15:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:15:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:43] train_sft.py:596 - step 4624: {'train/loss': 0.004540644586086273, 'train/grad_norm': 0.20553547143936157, 'train/lr_0': 3.7500000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:15:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:15:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:49] train_sft.py:596 - step 4625: {'train/loss': 0.005832054186612368, 'train/grad_norm': 0.3302854895591736, 'train/lr_0': 3.74e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 18:15:49] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:15:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:53] train_sft.py:596 - step 4626: {'train/loss': 0.007679997012019157, 'train/grad_norm': 0.18705400824546814, 'train/lr_0': 3.7300000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:15:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:15:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:15:58] train_sft.py:596 - step 4627: {'train/loss': 0.007956946268677711, 'train/grad_norm': 0.42399629950523376, 'train/lr_0': 3.7200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:15:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:15:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:03] train_sft.py:596 - step 4628: {'train/loss': 0.004777120426297188, 'train/grad_norm': 0.1369962990283966, 'train/lr_0': 3.7100000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:16:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:16:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:08] train_sft.py:596 - step 4629: {'train/loss': 0.00771308084949851, 'train/grad_norm': 0.5457953810691833, 'train/lr_0': 3.7e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 18:16:08] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:16:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:12] train_sft.py:596 - step 4630: {'train/loss': 0.005145017057657242, 'train/grad_norm': 0.19138525426387787, 'train/lr_0': 3.6900000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:16:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:16:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:17] train_sft.py:596 - step 4631: {'train/loss': 0.002976742573082447, 'train/grad_norm': 0.08336883038282394, 'train/lr_0': 3.6800000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:16:17] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:16:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:21] train_sft.py:596 - step 4632: {'train/loss': 0.0032813078723847866, 'train/grad_norm': 0.09575608372688293, 'train/lr_0': 3.6700000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:16:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:16:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:26] train_sft.py:596 - step 4633: {'train/loss': 0.005833765491843224, 'train/grad_norm': 0.2328033298254013, 'train/lr_0': 3.66e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:16:26] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:16:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:31] train_sft.py:596 - step 4634: {'train/loss': 0.006678078323602676, 'train/grad_norm': 0.23096337914466858, 'train/lr_0': 3.65e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 18:16:31] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:16:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:36] train_sft.py:596 - step 4635: {'train/loss': 0.007275067735463381, 'train/grad_norm': 0.4533076286315918, 'train/lr_0': 3.6400000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:16:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:16:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:41] train_sft.py:596 - step 4636: {'train/loss': 0.005508671514689922, 'train/grad_norm': 0.21071825921535492, 'train/lr_0': 3.6300000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:16:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:16:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:45] train_sft.py:596 - step 4637: {'train/loss': 0.0041604554280638695, 'train/grad_norm': 0.24055573344230652, 'train/lr_0': 3.62e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 18:16:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:16:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:50] train_sft.py:596 - step 4638: {'train/loss': 0.004714537411928177, 'train/grad_norm': 0.32633376121520996, 'train/lr_0': 3.61e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:16:50] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:16:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:16:55] train_sft.py:596 - step 4639: {'train/loss': 0.004338758997619152, 'train/grad_norm': 0.17377446591854095, 'train/lr_0': 3.6000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:16:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:16:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:00] train_sft.py:596 - step 4640: {'train/loss': 0.005202996078878641, 'train/grad_norm': 0.34612026810646057, 'train/lr_0': 3.5900000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:17:00] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:17:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:04] train_sft.py:596 - step 4641: {'train/loss': 0.006793180480599403, 'train/grad_norm': 0.4580558240413666, 'train/lr_0': 3.58e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:17:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:17:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:09] train_sft.py:596 - step 4642: {'train/loss': 0.004178265575319529, 'train/grad_norm': 0.2297922521829605, 'train/lr_0': 3.57e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:17:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:17:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:14] train_sft.py:596 - step 4643: {'train/loss': 0.003974530845880508, 'train/grad_norm': 0.2145289182662964, 'train/lr_0': 3.5600000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:17:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:17:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:19] train_sft.py:596 - step 4644: {'train/loss': 0.005882304161787033, 'train/grad_norm': 0.2954312264919281, 'train/lr_0': 3.5500000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:17:19] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:17:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:23] train_sft.py:596 - step 4645: {'train/loss': 0.003743387758731842, 'train/grad_norm': 0.20363229513168335, 'train/lr_0': 3.54e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:17:23] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:17:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:28] train_sft.py:596 - step 4646: {'train/loss': 0.008072813972830772, 'train/grad_norm': 0.45577383041381836, 'train/lr_0': 3.53e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:17:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:17:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:33] train_sft.py:596 - step 4647: {'train/loss': 0.004833556711673737, 'train/grad_norm': 0.32686471939086914, 'train/lr_0': 3.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:17:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:17:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:38] train_sft.py:596 - step 4648: {'train/loss': 0.0034582791849970818, 'train/grad_norm': 0.19711633026599884, 'train/lr_0': 3.5100000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:17:38] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:17:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:42] train_sft.py:596 - step 4649: {'train/loss': 0.00482889823615551, 'train/grad_norm': 0.5249073505401611, 'train/lr_0': 3.5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:17:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:17:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:47] train_sft.py:596 - step 4650: {'train/loss': 0.004363441839814186, 'train/grad_norm': 0.20889204740524292, 'train/lr_0': 3.49e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:17:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:17:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:52] train_sft.py:596 - step 4651: {'train/loss': 0.00545128807425499, 'train/grad_norm': 0.3682318329811096, 'train/lr_0': 3.48e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:17:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:17:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:17:56] train_sft.py:596 - step 4652: {'train/loss': 0.004501309245824814, 'train/grad_norm': 0.09064903110265732, 'train/lr_0': 3.4700000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:17:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:17:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:01] train_sft.py:596 - step 4653: {'train/loss': 0.005166724324226379, 'train/grad_norm': 0.3028143644332886, 'train/lr_0': 3.46e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:18:01] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:18:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:06] train_sft.py:596 - step 4654: {'train/loss': 0.006084564840421081, 'train/grad_norm': 0.35946327447891235, 'train/lr_0': 3.45e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:18:06] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:18:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:11] train_sft.py:596 - step 4655: {'train/loss': 0.005501244217157364, 'train/grad_norm': 0.21070337295532227, 'train/lr_0': 3.44e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 18:18:11] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:18:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:16] train_sft.py:596 - step 4656: {'train/loss': 0.008909844793379307, 'train/grad_norm': 0.40484529733657837, 'train/lr_0': 3.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 18:18:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:18:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:20] train_sft.py:596 - step 4657: {'train/loss': 0.005921628326177597, 'train/grad_norm': 0.17344816029071808, 'train/lr_0': 3.42e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:18:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:18:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:25] train_sft.py:596 - step 4658: {'train/loss': 0.006285412237048149, 'train/grad_norm': 0.23217101395130157, 'train/lr_0': 3.41e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:18:25] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:18:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:30] train_sft.py:596 - step 4659: {'train/loss': 0.007102878764271736, 'train/grad_norm': 0.6530135869979858, 'train/lr_0': 3.4e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 18:18:30] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:18:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:35] train_sft.py:596 - step 4660: {'train/loss': 0.005288959480822086, 'train/grad_norm': 0.3381948471069336, 'train/lr_0': 3.39e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:18:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:18:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:39] train_sft.py:596 - step 4661: {'train/loss': 0.008310677483677864, 'train/grad_norm': 0.2366713583469391, 'train/lr_0': 3.38e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:18:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:18:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:44] train_sft.py:596 - step 4662: {'train/loss': 0.005680976435542107, 'train/grad_norm': 0.2567383646965027, 'train/lr_0': 3.37e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:18:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:18:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:49] train_sft.py:596 - step 4663: {'train/loss': 0.00566572230309248, 'train/grad_norm': 0.23908090591430664, 'train/lr_0': 3.36e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:18:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:18:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:54] train_sft.py:596 - step 4664: {'train/loss': 0.011910265311598778, 'train/grad_norm': 0.25944405794143677, 'train/lr_0': 3.35e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 18:18:54] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:18:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:18:59] train_sft.py:596 - step 4665: {'train/loss': 0.0037237219512462616, 'train/grad_norm': 0.2704233229160309, 'train/lr_0': 3.3399999999999998e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 18:18:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:18:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:04] train_sft.py:596 - step 4666: {'train/loss': 0.003467282745987177, 'train/grad_norm': 0.23175984621047974, 'train/lr_0': 3.33e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
[2025-12-29 18:19:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:19:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:09] train_sft.py:596 - step 4667: {'train/loss': 0.004486924037337303, 'train/grad_norm': 0.22713114321231842, 'train/lr_0': 3.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:19:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:19:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:13] train_sft.py:596 - step 4668: {'train/loss': 0.005019734613597393, 'train/grad_norm': 0.23615172505378723, 'train/lr_0': 3.31e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:19:13] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:19:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:18] train_sft.py:596 - step 4669: {'train/loss': 0.005933711770921946, 'train/grad_norm': 0.22577910125255585, 'train/lr_0': 3.2999999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:19:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:19:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:23] train_sft.py:596 - step 4670: {'train/loss': 0.004367141984403133, 'train/grad_norm': 0.17976395785808563, 'train/lr_0': 3.29e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:19:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:19:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:28] train_sft.py:596 - step 4671: {'train/loss': 0.011698069050908089, 'train/grad_norm': 0.6535230278968811, 'train/lr_0': 3.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 18:19:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:19:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:33] train_sft.py:596 - step 4672: {'train/loss': 0.007683592848479748, 'train/grad_norm': 0.1669277548789978, 'train/lr_0': 3.27e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:19:33] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:19:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:37] train_sft.py:596 - step 4673: {'train/loss': 0.005713745951652527, 'train/grad_norm': 0.6658053994178772, 'train/lr_0': 3.2599999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:19:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:19:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:42] train_sft.py:596 - step 4674: {'train/loss': 0.005250217393040657, 'train/grad_norm': 0.1833498775959015, 'train/lr_0': 3.25e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:19:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:19:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:47] train_sft.py:596 - step 4675: {'train/loss': 0.004820896312594414, 'train/grad_norm': 0.18922673165798187, 'train/lr_0': 3.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 18:19:47] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:19:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:52] train_sft.py:596 - step 4676: {'train/loss': 0.005751999095082283, 'train/grad_norm': 0.39457303285598755, 'train/lr_0': 3.23e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:19:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:19:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:19:57] train_sft.py:596 - step 4677: {'train/loss': 0.0015118233859539032, 'train/grad_norm': 0.11580978333950043, 'train/lr_0': 3.2199999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:19:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:19:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:01] train_sft.py:596 - step 4678: {'train/loss': 0.005434589460492134, 'train/grad_norm': 0.1926642805337906, 'train/lr_0': 3.2099999999999998e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:20:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:20:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:06] train_sft.py:596 - step 4679: {'train/loss': 0.004715537652373314, 'train/grad_norm': 0.2689124047756195, 'train/lr_0': 3.2e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:20:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:20:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:11] train_sft.py:596 - step 4680: {'train/loss': 0.004524124786257744, 'train/grad_norm': 0.18546313047409058, 'train/lr_0': 3.19e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:20:11] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:20:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:16] train_sft.py:596 - step 4681: {'train/loss': 0.0037803910672664642, 'train/grad_norm': 0.2890700697898865, 'train/lr_0': 3.1799999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:20:16] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:20:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:21] train_sft.py:596 - step 4682: {'train/loss': 0.004170949338003993, 'train/grad_norm': 0.2047988325357437, 'train/lr_0': 3.1699999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:20:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:20:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:26] train_sft.py:596 - step 4683: {'train/loss': 0.005776305682957172, 'train/grad_norm': 0.26487141847610474, 'train/lr_0': 3.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:20:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:20:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:30] train_sft.py:596 - step 4684: {'train/loss': 0.006514562293887138, 'train/grad_norm': 0.3043117821216583, 'train/lr_0': 3.15e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 18:20:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:20:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:35] train_sft.py:596 - step 4685: {'train/loss': 0.00545146968215704, 'train/grad_norm': 0.27953460812568665, 'train/lr_0': 3.1399999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:20:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:20:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:39] train_sft.py:596 - step 4686: {'train/loss': 0.007432795129716396, 'train/grad_norm': 0.4185384511947632, 'train/lr_0': 3.1299999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 18:20:39] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:20:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:45] train_sft.py:596 - step 4687: {'train/loss': 0.010515104979276657, 'train/grad_norm': 0.6599545478820801, 'train/lr_0': 3.1200000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.91s/it]
[2025-12-29 18:20:45] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:20:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:50] train_sft.py:596 - step 4688: {'train/loss': 0.004199599847197533, 'train/grad_norm': 0.18404197692871094, 'train/lr_0': 3.1100000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.03s/it]
[2025-12-29 18:20:50] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 18:20:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:55] train_sft.py:596 - step 4689: {'train/loss': 0.005593575537204742, 'train/grad_norm': 0.2827792167663574, 'train/lr_0': 3.100000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:20:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:20:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:20:59] train_sft.py:596 - step 4690: {'train/loss': 0.0029485616832971573, 'train/grad_norm': 0.13135789334774017, 'train/lr_0': 3.090000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:20:59] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:21:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:04] train_sft.py:596 - step 4691: {'train/loss': 0.008510761894285679, 'train/grad_norm': 0.4299904704093933, 'train/lr_0': 3.0800000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 18:21:04] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:21:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:09] train_sft.py:596 - step 4692: {'train/loss': 0.007330790162086487, 'train/grad_norm': 0.3891807794570923, 'train/lr_0': 3.0700000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:21:09] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:21:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:14] train_sft.py:596 - step 4693: {'train/loss': 0.0011456292122602463, 'train/grad_norm': 0.02778913639485836, 'train/lr_0': 3.0600000000000008e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:21:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:21:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:19] train_sft.py:596 - step 4694: {'train/loss': 0.006976258009672165, 'train/grad_norm': 0.2734036445617676, 'train/lr_0': 3.050000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 18:21:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:21:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:23] train_sft.py:596 - step 4695: {'train/loss': 0.005837496370077133, 'train/grad_norm': 0.30659207701683044, 'train/lr_0': 3.0400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:21:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:21:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:28] train_sft.py:596 - step 4696: {'train/loss': 0.005955462343990803, 'train/grad_norm': 0.29733261466026306, 'train/lr_0': 3.0300000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:21:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:21:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:33] train_sft.py:596 - step 4697: {'train/loss': 0.009292302653193474, 'train/grad_norm': 0.37119415402412415, 'train/lr_0': 3.0200000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 18:21:33] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:21:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:38] train_sft.py:596 - step 4698: {'train/loss': 0.006183922290802002, 'train/grad_norm': 0.6204124689102173, 'train/lr_0': 3.010000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:21:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:21:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:42] train_sft.py:596 - step 4699: {'train/loss': 0.004881298169493675, 'train/grad_norm': 0.22043673694133759, 'train/lr_0': 3.0000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:21:42] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:21:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:47] train_sft.py:596 - step 4700: {'train/loss': 0.0014480818063020706, 'train/grad_norm': 0.09438920766115189, 'train/lr_0': 2.9900000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:21:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:21:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:52] train_sft.py:596 - step 4701: {'train/loss': 0.007113572210073471, 'train/grad_norm': 0.3124775290489197, 'train/lr_0': 2.9800000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:21:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:21:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:21:57] train_sft.py:596 - step 4702: {'train/loss': 0.00798729807138443, 'train/grad_norm': 0.33138445019721985, 'train/lr_0': 2.9700000000000008e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:21:57] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:21:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:01] train_sft.py:596 - step 4703: {'train/loss': 0.004693186841905117, 'train/grad_norm': 0.17820842564105988, 'train/lr_0': 2.9600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:22:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:22:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:06] train_sft.py:596 - step 4704: {'train/loss': 0.003010599873960018, 'train/grad_norm': 0.09402205049991608, 'train/lr_0': 2.9500000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:22:06] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:22:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:11] train_sft.py:596 - step 4705: {'train/loss': 0.010860724374651909, 'train/grad_norm': 0.6745516657829285, 'train/lr_0': 2.9400000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:22:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:22:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:15] train_sft.py:596 - step 4706: {'train/loss': 0.0036833714693784714, 'train/grad_norm': 0.2826195955276489, 'train/lr_0': 2.9300000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:22:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:22:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:20] train_sft.py:596 - step 4707: {'train/loss': 0.0038720835000276566, 'train/grad_norm': 0.16175149381160736, 'train/lr_0': 2.9200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:22:20] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:22:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:25] train_sft.py:596 - step 4708: {'train/loss': 0.009964211843907833, 'train/grad_norm': 0.4561026394367218, 'train/lr_0': 2.9100000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:22:25] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:22:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:30] train_sft.py:596 - step 4709: {'train/loss': 0.002183055505156517, 'train/grad_norm': 0.08892763406038284, 'train/lr_0': 2.9000000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:22:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:22:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:34] train_sft.py:596 - step 4710: {'train/loss': 0.00540471519343555, 'train/grad_norm': 0.3454153835773468, 'train/lr_0': 2.8900000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:22:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:22:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:39] train_sft.py:596 - step 4711: {'train/loss': 0.0026365071535110474, 'train/grad_norm': 0.2387440800666809, 'train/lr_0': 2.8800000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 18:22:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:22:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:44] train_sft.py:596 - step 4712: {'train/loss': 0.0056623853743076324, 'train/grad_norm': 0.294504851102829, 'train/lr_0': 2.8700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:22:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:22:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:49] train_sft.py:596 - step 4713: {'train/loss': 0.004398535005748272, 'train/grad_norm': 0.1862136274576187, 'train/lr_0': 2.8600000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:22:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:22:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:54] train_sft.py:596 - step 4714: {'train/loss': 0.006006179377436638, 'train/grad_norm': 0.2974943220615387, 'train/lr_0': 2.8500000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.22s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.22s/it]
[2025-12-29 18:22:54] timer.py:32 - Timer actor_train end (elapsed: 5.2s)
[2025-12-29 18:22:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:22:59] train_sft.py:596 - step 4715: {'train/loss': 0.005121242254972458, 'train/grad_norm': 0.27208662033081055, 'train/lr_0': 2.8400000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.86s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.87s/it]
[2025-12-29 18:22:59] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:23:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:04] train_sft.py:596 - step 4716: {'train/loss': 0.003360847942531109, 'train/grad_norm': 0.0844317376613617, 'train/lr_0': 2.8300000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 18:23:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:23:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:09] train_sft.py:596 - step 4717: {'train/loss': 0.006875887513160706, 'train/grad_norm': 0.30961331725120544, 'train/lr_0': 2.8200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:23:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:23:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:13] train_sft.py:596 - step 4718: {'train/loss': 0.004944413900375366, 'train/grad_norm': 0.16463537514209747, 'train/lr_0': 2.8100000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:23:13] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:23:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:18] train_sft.py:596 - step 4719: {'train/loss': 0.010314231738448143, 'train/grad_norm': 0.8835121393203735, 'train/lr_0': 2.8000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:23:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:23:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:23] train_sft.py:596 - step 4720: {'train/loss': 0.002735014073550701, 'train/grad_norm': 0.19393445551395416, 'train/lr_0': 2.7900000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:23:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:23:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:28] train_sft.py:596 - step 4721: {'train/loss': 0.012172311544418335, 'train/grad_norm': 0.4318123161792755, 'train/lr_0': 2.7800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 18:23:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:23:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:32] train_sft.py:596 - step 4722: {'train/loss': 0.003636562265455723, 'train/grad_norm': 0.17307588458061218, 'train/lr_0': 2.7700000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:23:32] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:23:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:37] train_sft.py:596 - step 4723: {'train/loss': 0.007022835547104478, 'train/grad_norm': 0.23064185678958893, 'train/lr_0': 2.7600000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:23:37] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:23:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:42] train_sft.py:596 - step 4724: {'train/loss': 0.007278217002749443, 'train/grad_norm': 0.28741925954818726, 'train/lr_0': 2.7500000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:23:42] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:23:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:47] train_sft.py:596 - step 4725: {'train/loss': 0.0051373946480453014, 'train/grad_norm': 0.14122870564460754, 'train/lr_0': 2.7400000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 18:23:47] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:23:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:51] train_sft.py:596 - step 4726: {'train/loss': 0.008597809821367264, 'train/grad_norm': 0.3432317078113556, 'train/lr_0': 2.7300000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:23:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:23:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:23:56] train_sft.py:596 - step 4727: {'train/loss': 0.010330942925065756, 'train/grad_norm': 0.20707017183303833, 'train/lr_0': 2.7200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.50s/it]
[2025-12-29 18:23:56] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:23:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:01] train_sft.py:596 - step 4728: {'train/loss': 0.003673514351248741, 'train/grad_norm': 0.16386626660823822, 'train/lr_0': 2.7100000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:24:01] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:24:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:06] train_sft.py:596 - step 4729: {'train/loss': 0.006037348881363869, 'train/grad_norm': 0.23517943918704987, 'train/lr_0': 2.7000000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:24:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:24:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:11] train_sft.py:596 - step 4730: {'train/loss': 0.008796529844403267, 'train/grad_norm': 0.6174484491348267, 'train/lr_0': 2.6900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 18:24:11] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:24:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:16] train_sft.py:596 - step 4731: {'train/loss': 0.0029056943021714687, 'train/grad_norm': 0.18613220751285553, 'train/lr_0': 2.68e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 18:24:16] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:24:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:20] train_sft.py:596 - step 4732: {'train/loss': 0.006603396497666836, 'train/grad_norm': 0.663723886013031, 'train/lr_0': 2.6700000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:24:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:24:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:26] train_sft.py:596 - step 4733: {'train/loss': 0.0009814528748393059, 'train/grad_norm': 0.011962180025875568, 'train/lr_0': 2.6600000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 18:24:26] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 18:24:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:30] train_sft.py:596 - step 4734: {'train/loss': 0.00345684215426445, 'train/grad_norm': 0.17610913515090942, 'train/lr_0': 2.6500000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:24:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:24:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:35] train_sft.py:596 - step 4735: {'train/loss': 0.0033078566193580627, 'train/grad_norm': 0.25683993101119995, 'train/lr_0': 2.64e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:24:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:24:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:40] train_sft.py:596 - step 4736: {'train/loss': 0.0020688604563474655, 'train/grad_norm': 0.01695314608514309, 'train/lr_0': 2.6300000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 18:24:40] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:24:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:45] train_sft.py:596 - step 4737: {'train/loss': 0.00607629120349884, 'train/grad_norm': 0.5079758763313293, 'train/lr_0': 2.6200000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:24:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:24:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:49] train_sft.py:596 - step 4738: {'train/loss': 0.0029448019340634346, 'train/grad_norm': 0.21641971170902252, 'train/lr_0': 2.6100000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:24:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:24:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:54] train_sft.py:596 - step 4739: {'train/loss': 0.004444329999387264, 'train/grad_norm': 0.1681683212518692, 'train/lr_0': 2.6e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:24:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:24:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:24:59] train_sft.py:596 - step 4740: {'train/loss': 0.007453250698745251, 'train/grad_norm': 0.7310221195220947, 'train/lr_0': 2.59e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 18:24:59] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:24:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:04] train_sft.py:596 - step 4741: {'train/loss': 0.0027445033192634583, 'train/grad_norm': 0.22290457785129547, 'train/lr_0': 2.5800000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:25:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:25:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:08] train_sft.py:596 - step 4742: {'train/loss': 0.0037501680199056864, 'train/grad_norm': 0.18219776451587677, 'train/lr_0': 2.5700000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:25:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:25:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:14] train_sft.py:596 - step 4743: {'train/loss': 0.0017831092700362206, 'train/grad_norm': 0.11002971231937408, 'train/lr_0': 2.56e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.85s/it]
[2025-12-29 18:25:14] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:25:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:18] train_sft.py:596 - step 4744: {'train/loss': 0.004214326851069927, 'train/grad_norm': 0.14357510209083557, 'train/lr_0': 2.55e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:25:18] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:25:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:23] train_sft.py:596 - step 4745: {'train/loss': 0.005646932870149612, 'train/grad_norm': 0.5795374512672424, 'train/lr_0': 2.5400000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 18:25:23] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:25:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:28] train_sft.py:596 - step 4746: {'train/loss': 0.006597411585971713, 'train/grad_norm': 0.3367249667644501, 'train/lr_0': 2.5300000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:25:28] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:25:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:32] train_sft.py:596 - step 4747: {'train/loss': 0.004245871677994728, 'train/grad_norm': 0.17067015171051025, 'train/lr_0': 2.52e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:25:32] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:25:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:37] train_sft.py:596 - step 4748: {'train/loss': 0.0009965109638869762, 'train/grad_norm': 0.014080275781452656, 'train/lr_0': 2.51e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:25:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:25:37] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:42] train_sft.py:596 - step 4749: {'train/loss': 0.007245616987347603, 'train/grad_norm': 0.1597345471382141, 'train/lr_0': 2.5e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:25:42] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:25:42] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:46] train_sft.py:596 - step 4750: {'train/loss': 0.004357334226369858, 'train/grad_norm': 0.13956472277641296, 'train/lr_0': 2.4900000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:25:46] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:25:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:51] train_sft.py:596 - step 4751: {'train/loss': 0.006469634361565113, 'train/grad_norm': 0.21813657879829407, 'train/lr_0': 2.4800000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:25:51] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:25:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:25:56] train_sft.py:596 - step 4752: {'train/loss': 0.0036609135568141937, 'train/grad_norm': 0.4348417818546295, 'train/lr_0': 2.47e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:25:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:25:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:00] train_sft.py:596 - step 4753: {'train/loss': 0.003216320415958762, 'train/grad_norm': 0.16117213666439056, 'train/lr_0': 2.46e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:26:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:26:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:05] train_sft.py:596 - step 4754: {'train/loss': 0.00282894354313612, 'train/grad_norm': 0.17526240646839142, 'train/lr_0': 2.4500000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 18:26:05] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:26:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:09] train_sft.py:596 - step 4755: {'train/loss': 0.004343394190073013, 'train/grad_norm': 0.25887933373451233, 'train/lr_0': 2.4400000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:26:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:26:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:14] train_sft.py:596 - step 4756: {'train/loss': 0.0030572875402867794, 'train/grad_norm': 0.4996858239173889, 'train/lr_0': 2.43e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.27s/it]
[2025-12-29 18:26:14] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:26:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:18] train_sft.py:596 - step 4757: {'train/loss': 0.005443139700219035, 'train/grad_norm': 0.3418869078159332, 'train/lr_0': 2.42e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:26:18] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:26:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:23] train_sft.py:596 - step 4758: {'train/loss': 0.0064216675236821175, 'train/grad_norm': 0.4943375289440155, 'train/lr_0': 2.4100000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:26:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:26:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:28] train_sft.py:596 - step 4759: {'train/loss': 0.005183199420571327, 'train/grad_norm': 0.22674661874771118, 'train/lr_0': 2.4000000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:26:28] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:26:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:33] train_sft.py:596 - step 4760: {'train/loss': 0.0012284405529499054, 'train/grad_norm': 0.030891643837094307, 'train/lr_0': 2.39e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 18:26:33] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:26:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:37] train_sft.py:596 - step 4761: {'train/loss': 0.0029767239466309547, 'train/grad_norm': 0.10923358798027039, 'train/lr_0': 2.38e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 18:26:37] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:26:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:42] train_sft.py:596 - step 4762: {'train/loss': 0.0029616495594382286, 'train/grad_norm': 0.16166426241397858, 'train/lr_0': 2.37e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 18:26:42] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:26:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:47] train_sft.py:596 - step 4763: {'train/loss': 0.0039923712611198425, 'train/grad_norm': 0.2994307279586792, 'train/lr_0': 2.3600000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:26:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:26:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:52] train_sft.py:596 - step 4764: {'train/loss': 0.007448690012097359, 'train/grad_norm': 0.2759872376918793, 'train/lr_0': 2.35e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:26:52] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:26:52] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:26:57] train_sft.py:596 - step 4765: {'train/loss': 0.001461084932088852, 'train/grad_norm': 0.031180087476968765, 'train/lr_0': 2.34e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:26:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:26:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:01] train_sft.py:596 - step 4766: {'train/loss': 0.004313410259783268, 'train/grad_norm': 0.2810432016849518, 'train/lr_0': 2.33e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:27:01] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:27:01] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:06] train_sft.py:596 - step 4767: {'train/loss': 0.00015244260430335999, 'train/grad_norm': 0.0055046696215868, 'train/lr_0': 2.3200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:27:06] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:27:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:11] train_sft.py:596 - step 4768: {'train/loss': 0.0052626244723796844, 'train/grad_norm': 0.1952381283044815, 'train/lr_0': 2.31e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 18:27:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:27:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:15] train_sft.py:596 - step 4769: {'train/loss': 0.0056398361921310425, 'train/grad_norm': 0.32616356015205383, 'train/lr_0': 2.3e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:27:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:27:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:20] train_sft.py:596 - step 4770: {'train/loss': 0.004566230811178684, 'train/grad_norm': 0.22785109281539917, 'train/lr_0': 2.29e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:27:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:27:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:24] train_sft.py:596 - step 4771: {'train/loss': 0.004005701746791601, 'train/grad_norm': 0.20442776381969452, 'train/lr_0': 2.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:27:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:27:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:29] train_sft.py:596 - step 4772: {'train/loss': 0.002231379970908165, 'train/grad_norm': 0.08434663712978363, 'train/lr_0': 2.27e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:27:29] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:27:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:34] train_sft.py:596 - step 4773: {'train/loss': 0.006586495786905289, 'train/grad_norm': 0.29415079951286316, 'train/lr_0': 2.26e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:27:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:27:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:39] train_sft.py:596 - step 4774: {'train/loss': 0.0018117176368832588, 'train/grad_norm': 0.11966641247272491, 'train/lr_0': 2.25e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:27:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:27:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:43] train_sft.py:596 - step 4775: {'train/loss': 0.004043068503960967, 'train/grad_norm': 0.2285982221364975, 'train/lr_0': 2.24e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:27:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:27:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:48] train_sft.py:596 - step 4776: {'train/loss': 0.004328103736042976, 'train/grad_norm': 0.20874592661857605, 'train/lr_0': 2.23e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:27:48] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:27:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:53] train_sft.py:596 - step 4777: {'train/loss': 0.004455123096704483, 'train/grad_norm': 0.15165509283542633, 'train/lr_0': 2.22e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 18:27:53] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:27:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:27:58] train_sft.py:596 - step 4778: {'train/loss': 0.004924175329506397, 'train/grad_norm': 0.3613807260990143, 'train/lr_0': 2.21e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:27:58] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:27:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:02] train_sft.py:596 - step 4779: {'train/loss': 0.002619431121274829, 'train/grad_norm': 0.23327600955963135, 'train/lr_0': 2.2e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:28:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:28:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:07] train_sft.py:596 - step 4780: {'train/loss': 0.0026267501525580883, 'train/grad_norm': 0.24987341463565826, 'train/lr_0': 2.1899999999999998e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:28:07] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:28:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:12] train_sft.py:596 - step 4781: {'train/loss': 0.001480132807046175, 'train/grad_norm': 0.18423844873905182, 'train/lr_0': 2.18e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:28:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:28:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:16] train_sft.py:596 - step 4782: {'train/loss': 0.007427171804010868, 'train/grad_norm': 0.2520518898963928, 'train/lr_0': 2.17e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 18:28:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:28:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:21] train_sft.py:596 - step 4783: {'train/loss': 0.002078227698802948, 'train/grad_norm': 0.08808068931102753, 'train/lr_0': 2.16e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:28:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:28:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:25] train_sft.py:596 - step 4784: {'train/loss': 0.0011649343650788069, 'train/grad_norm': 0.014563034288585186, 'train/lr_0': 2.1499999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:28:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:28:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:30] train_sft.py:596 - step 4785: {'train/loss': 0.0037752515636384487, 'train/grad_norm': 0.31525659561157227, 'train/lr_0': 2.14e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 18:28:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:28:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:35] train_sft.py:596 - step 4786: {'train/loss': 0.001695440267212689, 'train/grad_norm': 0.14995118975639343, 'train/lr_0': 2.13e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 18:28:35] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:28:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:40] train_sft.py:596 - step 4787: {'train/loss': 0.005788088776171207, 'train/grad_norm': 0.2859038710594177, 'train/lr_0': 2.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:28:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:28:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:45] train_sft.py:596 - step 4788: {'train/loss': 0.006139535456895828, 'train/grad_norm': 0.34202679991722107, 'train/lr_0': 2.1099999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:28:45] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:28:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:49] train_sft.py:596 - step 4789: {'train/loss': 0.002120525576174259, 'train/grad_norm': 0.16968759894371033, 'train/lr_0': 2.1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 18:28:49] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:28:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:54] train_sft.py:596 - step 4790: {'train/loss': 0.0034175257897004485, 'train/grad_norm': 0.32252737879753113, 'train/lr_0': 2.09e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:28:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:28:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:28:59] train_sft.py:596 - step 4791: {'train/loss': 0.0029724519699811935, 'train/grad_norm': 0.2543242871761322, 'train/lr_0': 2.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 18:28:59] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:28:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:03] train_sft.py:596 - step 4792: {'train/loss': 0.005323032848536968, 'train/grad_norm': 0.28801581263542175, 'train/lr_0': 2.0699999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:29:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:29:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:08] train_sft.py:596 - step 4793: {'train/loss': 0.001820066012442112, 'train/grad_norm': 0.12395604699850082, 'train/lr_0': 2.0599999999999998e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:29:08] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:29:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:13] train_sft.py:596 - step 4794: {'train/loss': 0.002501819282770157, 'train/grad_norm': 0.12959150969982147, 'train/lr_0': 2.05e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:29:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:29:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:17] train_sft.py:596 - step 4795: {'train/loss': 0.008767718449234962, 'train/grad_norm': 0.359332799911499, 'train/lr_0': 2.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:29:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:29:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:22] train_sft.py:596 - step 4796: {'train/loss': 0.005912563763558865, 'train/grad_norm': 0.3764079511165619, 'train/lr_0': 2.0299999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.25s/it]
[2025-12-29 18:29:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:29:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:27] train_sft.py:596 - step 4797: {'train/loss': 0.0036665089428424835, 'train/grad_norm': 0.13579177856445312, 'train/lr_0': 2.0199999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:29:27] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:29:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:31] train_sft.py:596 - step 4798: {'train/loss': 0.0029946286231279373, 'train/grad_norm': 0.09364818781614304, 'train/lr_0': 2.01e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.24s/it]
[2025-12-29 18:29:31] timer.py:32 - Timer actor_train end (elapsed: 4.2s)
[2025-12-29 18:29:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:36] train_sft.py:596 - step 4799: {'train/loss': 0.004551045596599579, 'train/grad_norm': 0.27827635407447815, 'train/lr_0': 2e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:29:36] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:29:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:41] train_sft.py:596 - step 4800: {'train/loss': 0.008348607458174229, 'train/grad_norm': 0.5172111392021179, 'train/lr_0': 1.9899999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:29:41] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:29:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:46] train_sft.py:596 - step 4801: {'train/loss': 0.006531696766614914, 'train/grad_norm': 0.5807533264160156, 'train/lr_0': 1.9799999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 18:29:46] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:29:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:51] train_sft.py:596 - step 4802: {'train/loss': 0.003504810156300664, 'train/grad_norm': 0.2971480190753937, 'train/lr_0': 1.9699999999999998e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:29:51] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:29:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:29:56] train_sft.py:596 - step 4803: {'train/loss': 0.003697676584124565, 'train/grad_norm': 0.1803876906633377, 'train/lr_0': 1.96e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:29:56] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:29:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:00] train_sft.py:596 - step 4804: {'train/loss': 0.0058525241911411285, 'train/grad_norm': 0.4188319146633148, 'train/lr_0': 1.9499999999999995e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:30:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:30:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:05] train_sft.py:596 - step 4805: {'train/loss': 0.0015539242886006832, 'train/grad_norm': 0.02195880189538002, 'train/lr_0': 1.9399999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:30:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:30:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:10] train_sft.py:596 - step 4806: {'train/loss': 0.011342485202476382, 'train/grad_norm': 0.5763738751411438, 'train/lr_0': 1.9299999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:30:10] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:30:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:14] train_sft.py:596 - step 4807: {'train/loss': 0.0013249608455225825, 'train/grad_norm': 0.017119182273745537, 'train/lr_0': 1.92e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:30:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:30:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:19] train_sft.py:596 - step 4808: {'train/loss': 0.0025317231193184853, 'train/grad_norm': 0.1439458131790161, 'train/lr_0': 1.9099999999999995e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:30:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:30:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:24] train_sft.py:596 - step 4809: {'train/loss': 0.0034344366285949945, 'train/grad_norm': 0.25746142864227295, 'train/lr_0': 1.8999999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:30:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:30:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:29] train_sft.py:596 - step 4810: {'train/loss': 0.004084387794137001, 'train/grad_norm': 0.18648695945739746, 'train/lr_0': 1.8899999999999997e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 18:30:29] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:30:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:34] train_sft.py:596 - step 4811: {'train/loss': 0.0020607211627066135, 'train/grad_norm': 0.09897716343402863, 'train/lr_0': 1.8799999999999996e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 18:30:34] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:30:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:39] train_sft.py:596 - step 4812: {'train/loss': 0.0019644545391201973, 'train/grad_norm': 0.035438600927591324, 'train/lr_0': 1.8700000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:30:39] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:30:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:44] train_sft.py:596 - step 4813: {'train/loss': 0.004614411853253841, 'train/grad_norm': 0.3957631587982178, 'train/lr_0': 1.8600000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 18:30:44] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:30:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:49] train_sft.py:596 - step 4814: {'train/loss': 0.007860463112592697, 'train/grad_norm': 0.3473854064941406, 'train/lr_0': 1.8500000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 18:30:49] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:30:49] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:53] train_sft.py:596 - step 4815: {'train/loss': 0.0023398473858833313, 'train/grad_norm': 0.16387607157230377, 'train/lr_0': 1.8400000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:30:53] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:30:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:30:58] train_sft.py:596 - step 4816: {'train/loss': 0.0032384064979851246, 'train/grad_norm': 0.06858158111572266, 'train/lr_0': 1.8300000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:30:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:30:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:02] train_sft.py:596 - step 4817: {'train/loss': 0.0034675835631787777, 'train/grad_norm': 0.17176416516304016, 'train/lr_0': 1.8200000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 18:31:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:31:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:07] train_sft.py:596 - step 4818: {'train/loss': 0.002560153603553772, 'train/grad_norm': 0.1916668564081192, 'train/lr_0': 1.8100000000000007e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:31:07] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:31:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:12] train_sft.py:596 - step 4819: {'train/loss': 0.004874187521636486, 'train/grad_norm': 0.07130614668130875, 'train/lr_0': 1.8000000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:31:12] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:31:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:17] train_sft.py:596 - step 4820: {'train/loss': 0.00409587100148201, 'train/grad_norm': 0.317905068397522, 'train/lr_0': 1.7900000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:31:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:31:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:22] train_sft.py:596 - step 4821: {'train/loss': 0.005832972005009651, 'train/grad_norm': 0.4964868426322937, 'train/lr_0': 1.7800000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:31:22] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:31:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:27] train_sft.py:596 - step 4822: {'train/loss': 0.004301751963794231, 'train/grad_norm': 0.34425273537635803, 'train/lr_0': 1.7700000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:31:27] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:31:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:31] train_sft.py:596 - step 4823: {'train/loss': 0.0023424215614795685, 'train/grad_norm': 0.08331368863582611, 'train/lr_0': 1.7600000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:31:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:31:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:36] train_sft.py:596 - step 4824: {'train/loss': 0.004917734302580357, 'train/grad_norm': 0.27627113461494446, 'train/lr_0': 1.7500000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:31:36] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:31:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:41] train_sft.py:596 - step 4825: {'train/loss': 0.002176712965592742, 'train/grad_norm': 0.22422216832637787, 'train/lr_0': 1.7400000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 18:31:41] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:31:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:45] train_sft.py:596 - step 4826: {'train/loss': 0.00336673716083169, 'train/grad_norm': 0.25164514780044556, 'train/lr_0': 1.7300000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:31:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:31:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:50] train_sft.py:596 - step 4827: {'train/loss': 0.003486075554974377, 'train/grad_norm': 0.2533855438232422, 'train/lr_0': 1.7200000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.71s/it]
[2025-12-29 18:31:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:31:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:31:55] train_sft.py:596 - step 4828: {'train/loss': 0.0019883201457560062, 'train/grad_norm': 0.2521839737892151, 'train/lr_0': 1.7100000000000006e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:31:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:31:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:00] train_sft.py:596 - step 4829: {'train/loss': 0.0036048744805157185, 'train/grad_norm': 0.3403260409832001, 'train/lr_0': 1.7000000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:32:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:32:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:04] train_sft.py:596 - step 4830: {'train/loss': 0.005092890001833439, 'train/grad_norm': 0.39622026681900024, 'train/lr_0': 1.6900000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:32:04] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:32:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:09] train_sft.py:596 - step 4831: {'train/loss': 0.007213059812784195, 'train/grad_norm': 0.4677185118198395, 'train/lr_0': 1.6800000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:32:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:32:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:14] train_sft.py:596 - step 4832: {'train/loss': 0.0009746416471898556, 'train/grad_norm': 0.045588139444589615, 'train/lr_0': 1.6700000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:32:14] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:32:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:19] train_sft.py:596 - step 4833: {'train/loss': 0.0010522531811147928, 'train/grad_norm': 0.05771535634994507, 'train/lr_0': 1.6600000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:32:19] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:32:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:23] train_sft.py:596 - step 4834: {'train/loss': 0.005106003023684025, 'train/grad_norm': 0.19147168099880219, 'train/lr_0': 1.6500000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.55s/it]
[2025-12-29 18:32:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:32:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:28] train_sft.py:596 - step 4835: {'train/loss': 0.001266779494471848, 'train/grad_norm': 0.11138967424631119, 'train/lr_0': 1.6400000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 18:32:28] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:32:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:33] train_sft.py:596 - step 4836: {'train/loss': 0.005677003413438797, 'train/grad_norm': 0.13728415966033936, 'train/lr_0': 1.6300000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:32:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:32:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:38] train_sft.py:596 - step 4837: {'train/loss': 0.0012439012061804533, 'train/grad_norm': 0.08438084274530411, 'train/lr_0': 1.6200000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 18:32:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:32:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:43] train_sft.py:596 - step 4838: {'train/loss': 0.005880851298570633, 'train/grad_norm': 0.2685105502605438, 'train/lr_0': 1.6100000000000005e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:32:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:32:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:47] train_sft.py:596 - step 4839: {'train/loss': 0.0029951645992696285, 'train/grad_norm': 0.22609300911426544, 'train/lr_0': 1.6000000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:32:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:32:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:52] train_sft.py:596 - step 4840: {'train/loss': 0.0028100013732910156, 'train/grad_norm': 0.06686071306467056, 'train/lr_0': 1.5900000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.79s/it]
[2025-12-29 18:32:52] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:32:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:32:57] train_sft.py:596 - step 4841: {'train/loss': 0.006550580263137817, 'train/grad_norm': 0.6280338168144226, 'train/lr_0': 1.5800000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:32:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:32:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:02] train_sft.py:596 - step 4842: {'train/loss': 0.005847534164786339, 'train/grad_norm': 0.22309382259845734, 'train/lr_0': 1.5700000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.70s/it]
[2025-12-29 18:33:02] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:33:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:07] train_sft.py:596 - step 4843: {'train/loss': 0.004942986648529768, 'train/grad_norm': 0.34180012345314026, 'train/lr_0': 1.5600000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:33:07] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:33:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:11] train_sft.py:596 - step 4844: {'train/loss': 0.0020934701897203922, 'train/grad_norm': 0.13069680333137512, 'train/lr_0': 1.5500000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:33:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:33:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:16] train_sft.py:596 - step 4845: {'train/loss': 0.004983394988812506, 'train/grad_norm': 0.2669835686683655, 'train/lr_0': 1.5400000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:33:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:33:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:21] train_sft.py:596 - step 4846: {'train/loss': 0.004151415079832077, 'train/grad_norm': 0.32223236560821533, 'train/lr_0': 1.5300000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:33:21] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:33:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:25] train_sft.py:596 - step 4847: {'train/loss': 0.0029645897448062897, 'train/grad_norm': 0.059753283858299255, 'train/lr_0': 1.5200000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:33:25] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:33:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:30] train_sft.py:596 - step 4848: {'train/loss': 0.005892133340239525, 'train/grad_norm': 0.2694317102432251, 'train/lr_0': 1.5100000000000004e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 18:33:30] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:33:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:35] train_sft.py:596 - step 4849: {'train/loss': 0.0034078583121299744, 'train/grad_norm': 0.11128173768520355, 'train/lr_0': 1.5000000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.26s/it]
[2025-12-29 18:33:35] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:33:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:40] train_sft.py:596 - step 4850: {'train/loss': 0.0035578906536102295, 'train/grad_norm': 0.2263009250164032, 'train/lr_0': 1.4900000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 18:33:40] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:33:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:45] train_sft.py:596 - step 4851: {'train/loss': 0.0054812803864479065, 'train/grad_norm': 0.23190467059612274, 'train/lr_0': 1.4800000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:33:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:33:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:49] train_sft.py:596 - step 4852: {'train/loss': 0.0031806547194719315, 'train/grad_norm': 0.2209022045135498, 'train/lr_0': 1.4700000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:33:49] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:33:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:54] train_sft.py:596 - step 4853: {'train/loss': 0.003102934220805764, 'train/grad_norm': 0.2583906650543213, 'train/lr_0': 1.4600000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:33:54] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:33:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:33:59] train_sft.py:596 - step 4854: {'train/loss': 0.006177953444421291, 'train/grad_norm': 0.393909215927124, 'train/lr_0': 1.4500000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:33:59] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:33:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:04] train_sft.py:596 - step 4855: {'train/loss': 0.006295203231275082, 'train/grad_norm': 0.3390887677669525, 'train/lr_0': 1.4400000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 18:34:04] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:34:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:09] train_sft.py:596 - step 4856: {'train/loss': 0.0045515361707657576, 'train/grad_norm': 0.2265344113111496, 'train/lr_0': 1.4300000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:34:09] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:34:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:13] train_sft.py:596 - step 4857: {'train/loss': 0.002105951076373458, 'train/grad_norm': 0.08904072642326355, 'train/lr_0': 1.4200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 18:34:13] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:34:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:18] train_sft.py:596 - step 4858: {'train/loss': 0.0029137288220226765, 'train/grad_norm': 0.291963130235672, 'train/lr_0': 1.4100000000000003e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:34:18] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:34:18] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:23] train_sft.py:596 - step 4859: {'train/loss': 0.0037036221474409103, 'train/grad_norm': 0.21041467785835266, 'train/lr_0': 1.4000000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:34:23] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:34:23] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:28] train_sft.py:596 - step 4860: {'train/loss': 0.005785929271951318, 'train/grad_norm': 0.44012680649757385, 'train/lr_0': 1.3900000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 18:34:28] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:34:28] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:33] train_sft.py:596 - step 4861: {'train/loss': 0.0032552294433116913, 'train/grad_norm': 0.13602383434772491, 'train/lr_0': 1.3800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:34:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:34:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:38] train_sft.py:596 - step 4862: {'train/loss': 0.0028484805952757597, 'train/grad_norm': 0.1328103244304657, 'train/lr_0': 1.3700000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:34:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:34:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:43] train_sft.py:596 - step 4863: {'train/loss': 0.0034940093755722046, 'train/grad_norm': 0.15394927561283112, 'train/lr_0': 1.3600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.65s/it]
[2025-12-29 18:34:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:34:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:47] train_sft.py:596 - step 4864: {'train/loss': 0.0038039986975491047, 'train/grad_norm': 0.21270006895065308, 'train/lr_0': 1.3500000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:34:47] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:34:47] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:52] train_sft.py:596 - step 4865: {'train/loss': 0.005133241880685091, 'train/grad_norm': 0.19329389929771423, 'train/lr_0': 1.34e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.90s/it]
[2025-12-29 18:34:52] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:34:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:34:57] train_sft.py:596 - step 4866: {'train/loss': 0.0034286295995116234, 'train/grad_norm': 0.0937616303563118, 'train/lr_0': 1.3300000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:34:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:34:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:02] train_sft.py:596 - step 4867: {'train/loss': 0.0035777827724814415, 'train/grad_norm': 0.2803908884525299, 'train/lr_0': 1.32e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.76s/it]
[2025-12-29 18:35:02] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:35:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:07] train_sft.py:596 - step 4868: {'train/loss': 0.0016109999269247055, 'train/grad_norm': 0.025657957419753075, 'train/lr_0': 1.3100000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:35:07] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:35:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:11] train_sft.py:596 - step 4869: {'train/loss': 0.003467351198196411, 'train/grad_norm': 0.2407505065202713, 'train/lr_0': 1.3e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:35:11] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:35:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:16] train_sft.py:596 - step 4870: {'train/loss': 0.0033990987576544285, 'train/grad_norm': 0.16131262481212616, 'train/lr_0': 1.2900000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:35:16] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:35:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:21] train_sft.py:596 - step 4871: {'train/loss': 0.003812044393271208, 'train/grad_norm': 0.16174262762069702, 'train/lr_0': 1.28e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 18:35:21] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:35:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:26] train_sft.py:596 - step 4872: {'train/loss': 0.005674097687005997, 'train/grad_norm': 0.4818958640098572, 'train/lr_0': 1.2700000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:35:26] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:35:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:31] train_sft.py:596 - step 4873: {'train/loss': 0.0040645357221364975, 'train/grad_norm': 0.354421466588974, 'train/lr_0': 1.26e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:35:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:35:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:36] train_sft.py:596 - step 4874: {'train/loss': 0.00474724336527288, 'train/grad_norm': 0.20344248414039612, 'train/lr_0': 1.25e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.99s/it]
[2025-12-29 18:35:36] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 18:35:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:41] train_sft.py:596 - step 4875: {'train/loss': 0.005827241926454008, 'train/grad_norm': 0.3980812430381775, 'train/lr_0': 1.2400000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:35:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:35:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:46] train_sft.py:596 - step 4876: {'train/loss': 0.01353181665763259, 'train/grad_norm': 0.9136284589767456, 'train/lr_0': 1.23e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.61s/it]
[2025-12-29 18:35:46] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:35:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:50] train_sft.py:596 - step 4877: {'train/loss': 0.003343451302498579, 'train/grad_norm': 0.23188158869743347, 'train/lr_0': 1.2200000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:35:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:35:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:35:55] train_sft.py:596 - step 4878: {'train/loss': 0.0032595070078969, 'train/grad_norm': 0.3168116807937622, 'train/lr_0': 1.21e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:35:55] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:35:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:00] train_sft.py:596 - step 4879: {'train/loss': 0.0027846950106322765, 'train/grad_norm': 0.10213661938905716, 'train/lr_0': 1.2000000000000002e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 18:36:00] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:36:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:05] train_sft.py:596 - step 4880: {'train/loss': 0.00636750552803278, 'train/grad_norm': 0.2557775378227234, 'train/lr_0': 1.19e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:36:05] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:36:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:10] train_sft.py:596 - step 4881: {'train/loss': 0.0019678654498420656, 'train/grad_norm': 0.10231803357601166, 'train/lr_0': 1.1800000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
[2025-12-29 18:36:10] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:36:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:15] train_sft.py:596 - step 4882: {'train/loss': 0.004991798661649227, 'train/grad_norm': 0.23187363147735596, 'train/lr_0': 1.17e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:36:15] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:36:15] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:20] train_sft.py:596 - step 4883: {'train/loss': 0.0018060291185975075, 'train/grad_norm': 0.06748037040233612, 'train/lr_0': 1.1600000000000001e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:36:20] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:36:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:24] train_sft.py:596 - step 4884: {'train/loss': 0.00569976563565433, 'train/grad_norm': 0.34211671352386475, 'train/lr_0': 1.15e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:36:24] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:36:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:29] train_sft.py:596 - step 4885: {'train/loss': 0.006395144388079643, 'train/grad_norm': 0.49442151188850403, 'train/lr_0': 1.14e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.56s/it]
[2025-12-29 18:36:29] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:36:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:34] train_sft.py:596 - step 4886: {'train/loss': 0.004450904903933406, 'train/grad_norm': 0.2170599400997162, 'train/lr_0': 1.13e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:36:34] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:36:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:39] train_sft.py:596 - step 4887: {'train/loss': 0.0036956483963876963, 'train/grad_norm': 0.25399401783943176, 'train/lr_0': 1.12e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:36:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:36:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:43] train_sft.py:596 - step 4888: {'train/loss': 0.007706483127549291, 'train/grad_norm': 0.4226233661174774, 'train/lr_0': 1.11e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:36:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:36:44] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:48] train_sft.py:596 - step 4889: {'train/loss': 0.0034214770421385765, 'train/grad_norm': 0.3468579053878784, 'train/lr_0': 1.1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.60s/it]
[2025-12-29 18:36:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:36:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:53] train_sft.py:596 - step 4890: {'train/loss': 0.001961447997018695, 'train/grad_norm': 0.05894700437784195, 'train/lr_0': 1.09e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.95s/it]
[2025-12-29 18:36:53] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:36:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:36:58] train_sft.py:596 - step 4891: {'train/loss': 0.0048746573738753796, 'train/grad_norm': 0.3850516080856323, 'train/lr_0': 1.08e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.66s/it]
[2025-12-29 18:36:58] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:36:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:03] train_sft.py:596 - step 4892: {'train/loss': 0.0030497033149003983, 'train/grad_norm': 0.13936124742031097, 'train/lr_0': 1.07e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:37:03] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:37:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:08] train_sft.py:596 - step 4893: {'train/loss': 0.0037905042991042137, 'train/grad_norm': 0.30033937096595764, 'train/lr_0': 1.06e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.59s/it]
[2025-12-29 18:37:08] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:37:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:12] train_sft.py:596 - step 4894: {'train/loss': 0.0029016705229878426, 'train/grad_norm': 0.31227579712867737, 'train/lr_0': 1.05e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:37:12] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:37:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:17] train_sft.py:596 - step 4895: {'train/loss': 0.0038363580824807286, 'train/grad_norm': 0.27359193563461304, 'train/lr_0': 1.04e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.46s/it]
[2025-12-29 18:37:17] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:37:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:22] train_sft.py:596 - step 4896: {'train/loss': 0.00206951517611742, 'train/grad_norm': 0.16994772851467133, 'train/lr_0': 1.0299999999999999e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:37:22] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:37:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:26] train_sft.py:596 - step 4897: {'train/loss': 0.007097338326275349, 'train/grad_norm': 0.40454089641571045, 'train/lr_0': 1.02e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:37:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:37:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:31] train_sft.py:596 - step 4898: {'train/loss': 0.006033472949638963, 'train/grad_norm': 0.484073281288147, 'train/lr_0': 1.0099999999999999e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:37:31] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:37:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:36] train_sft.py:596 - step 4899: {'train/loss': 0.008318615145981312, 'train/grad_norm': 0.6752862334251404, 'train/lr_0': 1e-06}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:37:36] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:37:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:41] train_sft.py:596 - step 4900: {'train/loss': 0.005349327111616731, 'train/grad_norm': 0.26428940892219543, 'train/lr_0': 9.899999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:37:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:37:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:45] train_sft.py:596 - step 4901: {'train/loss': 0.004723058082163334, 'train/grad_norm': 0.22328220307826996, 'train/lr_0': 9.8e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:37:45] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:37:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:50] train_sft.py:596 - step 4902: {'train/loss': 0.002613561227917671, 'train/grad_norm': 0.1805953085422516, 'train/lr_0': 9.699999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:37:50] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:37:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:37:55] train_sft.py:596 - step 4903: {'train/loss': 0.0031476281583309174, 'train/grad_norm': 0.3064521253108978, 'train/lr_0': 9.6e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 18:37:55] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:37:56] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:00] train_sft.py:596 - step 4904: {'train/loss': 0.005918427370488644, 'train/grad_norm': 0.19015547633171082, 'train/lr_0': 9.499999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:38:00] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:38:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:05] train_sft.py:596 - step 4905: {'train/loss': 0.005893602967262268, 'train/grad_norm': 0.2935137450695038, 'train/lr_0': 9.399999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:38:05] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:38:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:09] train_sft.py:596 - step 4906: {'train/loss': 0.006754837930202484, 'train/grad_norm': 0.4218892753124237, 'train/lr_0': 9.299999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:38:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:38:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:14] train_sft.py:596 - step 4907: {'train/loss': 0.002242111600935459, 'train/grad_norm': 0.24523033201694489, 'train/lr_0': 9.199999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:38:14] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:38:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:19] train_sft.py:596 - step 4908: {'train/loss': 0.0047299632569774985, 'train/grad_norm': 0.28220751881599426, 'train/lr_0': 9.099999999999998e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:38:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:38:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:24] train_sft.py:596 - step 4909: {'train/loss': 0.004278289387002587, 'train/grad_norm': 0.16935375332832336, 'train/lr_0': 8.999999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:38:24] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:38:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:28] train_sft.py:596 - step 4910: {'train/loss': 0.0045655915746465325, 'train/grad_norm': 0.1730063259601593, 'train/lr_0': 8.899999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:38:28] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:38:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:34] train_sft.py:596 - step 4911: {'train/loss': 0.006212376058101654, 'train/grad_norm': 0.4329109191894531, 'train/lr_0': 8.799999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.93s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.94s/it]
[2025-12-29 18:38:34] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:38:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:38] train_sft.py:596 - step 4912: {'train/loss': 0.003772463765926659, 'train/grad_norm': 0.27670472860336304, 'train/lr_0': 8.699999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:38:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:38:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:43] train_sft.py:596 - step 4913: {'train/loss': 0.006027695431839675, 'train/grad_norm': 0.2946123480796814, 'train/lr_0': 8.599999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:38:43] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:38:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:47] train_sft.py:596 - step 4914: {'train/loss': 0.0023100683465600014, 'train/grad_norm': 0.048545412719249725, 'train/lr_0': 8.499999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:38:47] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:38:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:52] train_sft.py:596 - step 4915: {'train/loss': 0.002652962226420641, 'train/grad_norm': 0.29994872212409973, 'train/lr_0': 8.399999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:38:52] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:38:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:38:57] train_sft.py:596 - step 4916: {'train/loss': 0.008574106497690082, 'train/grad_norm': 0.3632690906524658, 'train/lr_0': 8.299999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:38:57] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:38:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:02] train_sft.py:596 - step 4917: {'train/loss': 0.00560131436213851, 'train/grad_norm': 0.23294293880462646, 'train/lr_0': 8.199999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:39:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:39:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:07] train_sft.py:596 - step 4918: {'train/loss': 0.004425393417477608, 'train/grad_norm': 0.26445916295051575, 'train/lr_0': 8.099999999999997e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.82s/it]
[2025-12-29 18:39:07] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:39:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:12] train_sft.py:596 - step 4919: {'train/loss': 0.004121318110264838, 'train/grad_norm': 0.18626663088798523, 'train/lr_0': 7.999999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.52s/it]
[2025-12-29 18:39:12] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:39:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:16] train_sft.py:596 - step 4920: {'train/loss': 0.003484062384814024, 'train/grad_norm': 0.2667103111743927, 'train/lr_0': 7.899999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:39:16] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:39:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:21] train_sft.py:596 - step 4921: {'train/loss': 0.006293245241977274, 'train/grad_norm': 0.3435075581073761, 'train/lr_0': 7.799999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:39:21] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:39:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:26] train_sft.py:596 - step 4922: {'train/loss': 0.0025119464844465256, 'train/grad_norm': 0.12855730950832367, 'train/lr_0': 7.699999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:39:26] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:39:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:30] train_sft.py:596 - step 4923: {'train/loss': 0.006769177503883839, 'train/grad_norm': 0.3195323944091797, 'train/lr_0': 7.599999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.47s/it]
[2025-12-29 18:39:30] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:39:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:35] train_sft.py:596 - step 4924: {'train/loss': 0.006370233371853828, 'train/grad_norm': 0.4442542493343353, 'train/lr_0': 7.499999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:39:35] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:39:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:40] train_sft.py:596 - step 4925: {'train/loss': 0.004615987651050091, 'train/grad_norm': 0.4138301610946655, 'train/lr_0': 7.399999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.98s/it]
[2025-12-29 18:39:40] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 18:39:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:45] train_sft.py:596 - step 4926: {'train/loss': 0.0039074900560081005, 'train/grad_norm': 0.37316370010375977, 'train/lr_0': 7.299999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.51s/it]
[2025-12-29 18:39:45] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:39:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:50] train_sft.py:596 - step 4927: {'train/loss': 0.0044654542580246925, 'train/grad_norm': 0.3457811176776886, 'train/lr_0': 7.199999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.06s/it]
[2025-12-29 18:39:50] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 18:39:51] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:39:55] train_sft.py:596 - step 4928: {'train/loss': 0.004606989212334156, 'train/grad_norm': 0.3462783694267273, 'train/lr_0': 7.099999999999996e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
[2025-12-29 18:39:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:39:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:00] train_sft.py:596 - step 4929: {'train/loss': 0.004213753156363964, 'train/grad_norm': 0.17857202887535095, 'train/lr_0': 6.999999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:40:00] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:00] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:05] train_sft.py:596 - step 4930: {'train/loss': 0.003061826340854168, 'train/grad_norm': 0.13056331872940063, 'train/lr_0': 6.899999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.64s/it]
[2025-12-29 18:40:05] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:40:05] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:09] train_sft.py:596 - step 4931: {'train/loss': 0.0020354711450636387, 'train/grad_norm': 0.09861055761575699, 'train/lr_0': 6.799999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:40:09] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:10] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:14] train_sft.py:596 - step 4932: {'train/loss': 0.0029506394639611244, 'train/grad_norm': 0.024934209883213043, 'train/lr_0': 6.699999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:40:14] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:19] train_sft.py:596 - step 4933: {'train/loss': 0.0032380339689552784, 'train/grad_norm': 0.10085881501436234, 'train/lr_0': 6.599999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:40:19] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:23] train_sft.py:596 - step 4934: {'train/loss': 0.001243754755705595, 'train/grad_norm': 0.14012262225151062, 'train/lr_0': 6.499999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.43s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.44s/it]
[2025-12-29 18:40:23] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:28] train_sft.py:596 - step 4935: {'train/loss': 0.004356819437816739, 'train/grad_norm': 0.2039405107498169, 'train/lr_0': 6.399999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 18:40:28] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:40:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:33] train_sft.py:596 - step 4936: {'train/loss': 0.0030054068192839622, 'train/grad_norm': 0.14024627208709717, 'train/lr_0': 6.299999999999995e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:40:33] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:33] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:38] train_sft.py:596 - step 4937: {'train/loss': 0.0037799347192049026, 'train/grad_norm': 0.1504572331905365, 'train/lr_0': 6.200000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.67s/it]
[2025-12-29 18:40:38] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:40:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:43] train_sft.py:596 - step 4938: {'train/loss': 0.0026180767454206944, 'train/grad_norm': 0.03929649665951729, 'train/lr_0': 6.100000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.62s/it]
[2025-12-29 18:40:43] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:40:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:48] train_sft.py:596 - step 4939: {'train/loss': 0.004191541578620672, 'train/grad_norm': 0.1969272345304489, 'train/lr_0': 6.000000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.57s/it]
[2025-12-29 18:40:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:40:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:52] train_sft.py:596 - step 4940: {'train/loss': 0.002355957869440317, 'train/grad_norm': 0.07717344164848328, 'train/lr_0': 5.900000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:40:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:40:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:40:57] train_sft.py:596 - step 4941: {'train/loss': 0.004942970350384712, 'train/grad_norm': 0.20957224071025848, 'train/lr_0': 5.800000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:40:57] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:40:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:01] train_sft.py:596 - step 4942: {'train/loss': 0.007251848001033068, 'train/grad_norm': 0.38853487372398376, 'train/lr_0': 5.700000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:41:01] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:41:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:06] train_sft.py:596 - step 4943: {'train/loss': 0.005511582654435188, 'train/grad_norm': 0.29884475469589233, 'train/lr_0': 5.600000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:41:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:41:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:11] train_sft.py:596 - step 4944: {'train/loss': 0.009330324362963438, 'train/grad_norm': 0.31421613693237305, 'train/lr_0': 5.500000000000006e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:41:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:41:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:15] train_sft.py:596 - step 4945: {'train/loss': 0.0031826928025111556, 'train/grad_norm': 0.21962615847587585, 'train/lr_0': 5.400000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:41:15] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:41:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:20] train_sft.py:596 - step 4946: {'train/loss': 0.007022810750640929, 'train/grad_norm': 0.37429291009902954, 'train/lr_0': 5.300000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:41:20] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:41:20] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:24] train_sft.py:596 - step 4947: {'train/loss': 0.005376581102609634, 'train/grad_norm': 0.3436814844608307, 'train/lr_0': 5.200000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:41:24] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:41:25] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:29] train_sft.py:596 - step 4948: {'train/loss': 0.00161896797362715, 'train/grad_norm': 0.05374123901128769, 'train/lr_0': 5.100000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.54s/it]
[2025-12-29 18:41:29] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:41:30] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:34] train_sft.py:596 - step 4949: {'train/loss': 0.004864937160164118, 'train/grad_norm': 0.2957862913608551, 'train/lr_0': 5.000000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:41:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:41:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:39] train_sft.py:596 - step 4950: {'train/loss': 0.004180818563327193, 'train/grad_norm': 0.2340478003025055, 'train/lr_0': 4.900000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:41:39] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:41:39] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:43] train_sft.py:596 - step 4951: {'train/loss': 0.002674332819879055, 'train/grad_norm': 0.15089182555675507, 'train/lr_0': 4.800000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:41:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:41:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:48] train_sft.py:596 - step 4952: {'train/loss': 0.0036564087495207787, 'train/grad_norm': 0.14147502183914185, 'train/lr_0': 4.700000000000005e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.39s/it]
[2025-12-29 18:41:48] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:41:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:52] train_sft.py:596 - step 4953: {'train/loss': 0.0015516135608777404, 'train/grad_norm': 0.029809897765517235, 'train/lr_0': 4.6000000000000047e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:41:52] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:41:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:41:58] train_sft.py:596 - step 4954: {'train/loss': 0.0013152802421245724, 'train/grad_norm': 0.04009618982672691, 'train/lr_0': 4.5000000000000046e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.96s/it]
[2025-12-29 18:41:58] timer.py:32 - Timer actor_train end (elapsed: 5.0s)
[2025-12-29 18:41:58] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:02] train_sft.py:596 - step 4955: {'train/loss': 0.0031843241304159164, 'train/grad_norm': 0.057593606412410736, 'train/lr_0': 4.4000000000000045e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:42:02] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:42:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:07] train_sft.py:596 - step 4956: {'train/loss': 0.007016292307525873, 'train/grad_norm': 0.4566931426525116, 'train/lr_0': 4.3000000000000044e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:42:07] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:42:07] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:12] train_sft.py:596 - step 4957: {'train/loss': 0.0016054725274443626, 'train/grad_norm': 0.13494087755680084, 'train/lr_0': 4.200000000000004e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:42:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:42:12] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:17] train_sft.py:596 - step 4958: {'train/loss': 0.002663041464984417, 'train/grad_norm': 0.12095219641923904, 'train/lr_0': 4.100000000000004e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.74s/it]
[2025-12-29 18:42:17] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:42:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:21] train_sft.py:596 - step 4959: {'train/loss': 0.005109605146571994, 'train/grad_norm': 0.35328954458236694, 'train/lr_0': 4.000000000000004e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.28s/it]
[2025-12-29 18:42:21] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:42:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:26] train_sft.py:596 - step 4960: {'train/loss': 0.005246250424534082, 'train/grad_norm': 0.30404579639434814, 'train/lr_0': 3.900000000000004e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:42:26] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:42:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:30] train_sft.py:596 - step 4961: {'train/loss': 0.0014512566849589348, 'train/grad_norm': 0.1027989387512207, 'train/lr_0': 3.800000000000004e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
[2025-12-29 18:42:30] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:42:31] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:35] train_sft.py:596 - step 4962: {'train/loss': 0.004178856965154409, 'train/grad_norm': 0.2348822057247162, 'train/lr_0': 3.700000000000004e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:42:35] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:42:35] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:40] train_sft.py:596 - step 4963: {'train/loss': 0.0021068797796033323, 'train/grad_norm': 0.14198647439479828, 'train/lr_0': 3.6000000000000036e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:42:40] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:42:40] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:45] train_sft.py:596 - step 4964: {'train/loss': 0.0029197423718869686, 'train/grad_norm': 0.07926523685455322, 'train/lr_0': 3.5000000000000035e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.73s/it]
[2025-12-29 18:42:45] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:42:45] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:49] train_sft.py:596 - step 4965: {'train/loss': 0.0031020198366604745, 'train/grad_norm': 0.28996387124061584, 'train/lr_0': 3.4000000000000034e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:42:49] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:42:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:54] train_sft.py:596 - step 4966: {'train/loss': 0.001117318868637085, 'train/grad_norm': 0.14689189195632935, 'train/lr_0': 3.3000000000000033e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:42:54] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:42:54] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:42:58] train_sft.py:596 - step 4967: {'train/loss': 0.0032914765179157257, 'train/grad_norm': 0.21522893011569977, 'train/lr_0': 3.200000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:42:58] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:42:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:03] train_sft.py:596 - step 4968: {'train/loss': 0.002939830534160137, 'train/grad_norm': 0.13427886366844177, 'train/lr_0': 3.100000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.29s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.30s/it]
[2025-12-29 18:43:03] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:43:03] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:08] train_sft.py:596 - step 4969: {'train/loss': 0.005581680219620466, 'train/grad_norm': 0.6143311262130737, 'train/lr_0': 3.000000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:43:08] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:43:08] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:12] train_sft.py:596 - step 4970: {'train/loss': 0.0008590342476963997, 'train/grad_norm': 0.05036453530192375, 'train/lr_0': 2.900000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:43:12] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:43:13] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:17] train_sft.py:596 - step 4971: {'train/loss': 0.006095308810472488, 'train/grad_norm': 0.3200533986091614, 'train/lr_0': 2.800000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.58s/it]
[2025-12-29 18:43:17] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:43:17] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:22] train_sft.py:596 - step 4972: {'train/loss': 0.0034173016902059317, 'train/grad_norm': 0.10933715850114822, 'train/lr_0': 2.700000000000003e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:43:22] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:43:22] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:27] train_sft.py:596 - step 4973: {'train/loss': 0.0029413053998723626, 'train/grad_norm': 0.10692187398672104, 'train/lr_0': 2.6000000000000026e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.53s/it]
[2025-12-29 18:43:27] timer.py:32 - Timer actor_train end (elapsed: 4.5s)
[2025-12-29 18:43:27] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:31] train_sft.py:596 - step 4974: {'train/loss': 0.0036319890059530735, 'train/grad_norm': 0.22991131246089935, 'train/lr_0': 2.5000000000000025e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:43:31] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:43:32] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:36] train_sft.py:596 - step 4975: {'train/loss': 0.003974022809416056, 'train/grad_norm': 0.22305206954479218, 'train/lr_0': 2.4000000000000024e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:43:36] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:43:36] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:41] train_sft.py:596 - step 4976: {'train/loss': 0.002417163224890828, 'train/grad_norm': 0.1697755753993988, 'train/lr_0': 2.3000000000000023e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:43:41] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:43:41] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:45] train_sft.py:596 - step 4977: {'train/loss': 0.0051203909097239375, 'train/grad_norm': 0.3253106474876404, 'train/lr_0': 2.2000000000000022e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.31s/it]
[2025-12-29 18:43:45] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:43:46] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:50] train_sft.py:596 - step 4978: {'train/loss': 0.0013331654481589794, 'train/grad_norm': 0.13854044675827026, 'train/lr_0': 2.100000000000002e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.38s/it]
[2025-12-29 18:43:50] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:43:50] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:55] train_sft.py:596 - step 4979: {'train/loss': 0.00318324426189065, 'train/grad_norm': 0.30626440048217773, 'train/lr_0': 2.000000000000002e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.37s/it]
[2025-12-29 18:43:55] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:43:55] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:43:59] train_sft.py:596 - step 4980: {'train/loss': 0.00385562633164227, 'train/grad_norm': 0.19968105852603912, 'train/lr_0': 1.900000000000002e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:43:59] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:43:59] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:04] train_sft.py:596 - step 4981: {'train/loss': 0.005263288505375385, 'train/grad_norm': 0.3641158640384674, 'train/lr_0': 1.8000000000000018e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.34s/it]
[2025-12-29 18:44:04] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:44:04] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:09] train_sft.py:596 - step 4982: {'train/loss': 0.001599232666194439, 'train/grad_norm': 0.16549408435821533, 'train/lr_0': 1.7000000000000017e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.68s/it]
[2025-12-29 18:44:09] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:44:09] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:14] train_sft.py:596 - step 4983: {'train/loss': 0.003282691817730665, 'train/grad_norm': 0.12742970883846283, 'train/lr_0': 1.6000000000000016e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.84s/it]
[2025-12-29 18:44:14] timer.py:32 - Timer actor_train end (elapsed: 4.8s)
[2025-12-29 18:44:14] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:19] train_sft.py:596 - step 4984: {'train/loss': 0.0013173953630030155, 'train/grad_norm': 0.12965163588523865, 'train/lr_0': 1.5000000000000015e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.89s/it]
[2025-12-29 18:44:19] timer.py:32 - Timer actor_train end (elapsed: 4.9s)
[2025-12-29 18:44:19] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:24] train_sft.py:596 - step 4985: {'train/loss': 0.0037823282182216644, 'train/grad_norm': 0.11546169966459274, 'train/lr_0': 1.4000000000000014e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.42s/it]
[2025-12-29 18:44:24] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:44:24] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:29] train_sft.py:596 - step 4986: {'train/loss': 0.005516489618457854, 'train/grad_norm': 0.18891482055187225, 'train/lr_0': 1.3000000000000013e-07}

actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
actor_train: 100%|██████████| 1/1 [00:05<00:00,  5.08s/it]
[2025-12-29 18:44:29] timer.py:32 - Timer actor_train end (elapsed: 5.1s)
[2025-12-29 18:44:29] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:34] train_sft.py:596 - step 4987: {'train/loss': 0.004392354050651193, 'train/grad_norm': 0.35253390669822693, 'train/lr_0': 1.2000000000000012e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:44:34] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:44:34] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:38] train_sft.py:596 - step 4988: {'train/loss': 0.0020049037411808968, 'train/grad_norm': 0.0747530460357666, 'train/lr_0': 1.1000000000000011e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.32s/it]
[2025-12-29 18:44:38] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:44:38] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:43] train_sft.py:596 - step 4989: {'train/loss': 0.00326437468174845, 'train/grad_norm': 0.10073264688253403, 'train/lr_0': 1.000000000000001e-07}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.36s/it]
[2025-12-29 18:44:43] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:44:43] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:48] train_sft.py:596 - step 4990: {'train/loss': 0.0039426113944500685, 'train/grad_norm': 0.27694758772850037, 'train/lr_0': 9.000000000000009e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.63s/it]
[2025-12-29 18:44:48] timer.py:32 - Timer actor_train end (elapsed: 4.6s)
[2025-12-29 18:44:48] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:52] train_sft.py:596 - step 4991: {'train/loss': 0.0020074760541319847, 'train/grad_norm': 0.11652122437953949, 'train/lr_0': 8.000000000000008e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:44:52] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:44:53] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:44:57] train_sft.py:596 - step 4992: {'train/loss': 0.0017762468196451664, 'train/grad_norm': 0.15195761620998383, 'train/lr_0': 7.000000000000007e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:44:57] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:44:57] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:02] train_sft.py:596 - step 4993: {'train/loss': 0.0015465808100998402, 'train/grad_norm': 0.08250481635332108, 'train/lr_0': 6.000000000000006e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.33s/it]
[2025-12-29 18:45:02] timer.py:32 - Timer actor_train end (elapsed: 4.3s)
[2025-12-29 18:45:02] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:06] train_sft.py:596 - step 4994: {'train/loss': 0.0013053864240646362, 'train/grad_norm': 0.17592431604862213, 'train/lr_0': 5.000000000000005e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:45:06] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:45:06] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:11] train_sft.py:596 - step 4995: {'train/loss': 0.003427480347454548, 'train/grad_norm': 0.16028663516044617, 'train/lr_0': 4.000000000000004e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.40s/it]
[2025-12-29 18:45:11] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:45:11] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:15] train_sft.py:596 - step 4996: {'train/loss': 0.003969299606978893, 'train/grad_norm': 0.2894793450832367, 'train/lr_0': 3.000000000000003e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.35s/it]
[2025-12-29 18:45:15] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:45:16] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:20] train_sft.py:596 - step 4997: {'train/loss': 0.0017510205507278442, 'train/grad_norm': 0.030856046825647354, 'train/lr_0': 2.000000000000002e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.69s/it]
[2025-12-29 18:45:20] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:45:21] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:25] train_sft.py:596 - step 4998: {'train/loss': 0.004015025217086077, 'train/grad_norm': 0.26723355054855347, 'train/lr_0': 1.000000000000001e-08}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.72s/it]
[2025-12-29 18:45:25] timer.py:32 - Timer actor_train end (elapsed: 4.7s)
[2025-12-29 18:45:26] timer.py:24 - Timer actor_train start

actor_train:   0%|          | 0/1 [00:00<?, ?it/s][2025-12-29 18:45:30] train_sft.py:596 - step 4999: {'train/loss': 0.0012818698305636644, 'train/grad_norm': 0.09360909461975098, 'train/lr_0': 0.0}

actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
actor_train: 100%|██████████| 1/1 [00:04<00:00,  4.41s/it]
[2025-12-29 18:45:30] timer.py:32 - Timer actor_train end (elapsed: 4.4s)
[2025-12-29 18:45:42] checkpoint.py:287 - [FSDP] Saved checkpoint to /p/scratch/envcomp/mihir/huggingface/shared_data/13034357/checkpoints/iter_0005000

actor_val:   0%|          | 0/100 [00:00<?, ?it/s]
actor_val:   1%|          | 1/100 [00:02<04:32,  2.75s/it]
actor_val:   2%|▏         | 2/100 [00:04<03:37,  2.22s/it]
actor_val:   3%|▎         | 3/100 [00:06<03:17,  2.03s/it]
actor_val:   4%|▍         | 4/100 [00:07<02:55,  1.83s/it]
actor_val:   5%|▌         | 5/100 [00:09<02:53,  1.82s/it]
actor_val:   6%|▌         | 6/100 [00:11<02:43,  1.73s/it]
actor_val:   7%|▋         | 7/100 [00:12<02:35,  1.67s/it]
actor_val:   8%|▊         | 8/100 [00:14<02:37,  1.71s/it]
actor_val:   9%|▉         | 9/100 [00:16<02:31,  1.66s/it]
actor_val:  10%|█         | 10/100 [00:17<02:27,  1.63s/it]
actor_val:  11%|█         | 11/100 [00:19<02:22,  1.60s/it]
actor_val:  12%|█▏        | 12/100 [00:21<02:27,  1.67s/it]
actor_val:  13%|█▎        | 13/100 [00:22<02:22,  1.64s/it]
actor_val:  14%|█▍        | 14/100 [00:24<02:26,  1.70s/it]
actor_val:  15%|█▌        | 15/100 [00:26<02:27,  1.73s/it]
actor_val:  16%|█▌        | 16/100 [00:27<02:20,  1.68s/it]
actor_val:  17%|█▋        | 17/100 [00:29<02:15,  1.63s/it]
actor_val:  18%|█▊        | 18/100 [00:30<02:11,  1.60s/it]
actor_val:  19%|█▉        | 19/100 [00:32<02:08,  1.58s/it]
actor_val:  20%|██        | 20/100 [00:34<02:12,  1.65s/it]
actor_val:  21%|██        | 21/100 [00:36<02:13,  1.69s/it]
actor_val:  22%|██▏       | 22/100 [00:37<02:08,  1.65s/it]
actor_val:  23%|██▎       | 23/100 [00:39<02:04,  1.62s/it]
actor_val:  24%|██▍       | 24/100 [00:40<02:01,  1.60s/it]
actor_val:  25%|██▌       | 25/100 [00:42<02:05,  1.67s/it]
actor_val:  26%|██▌       | 26/100 [00:44<02:01,  1.65s/it]
actor_val:  27%|██▋       | 27/100 [00:45<01:57,  1.61s/it]
actor_val:  28%|██▊       | 28/100 [00:47<01:54,  1.59s/it]
actor_val:  29%|██▉       | 29/100 [00:48<01:52,  1.58s/it]
actor_val:  30%|███       | 30/100 [00:50<01:50,  1.58s/it]
actor_val:  31%|███       | 31/100 [00:51<01:46,  1.55s/it]
actor_val:  32%|███▏      | 32/100 [00:53<01:44,  1.54s/it]
actor_val:  33%|███▎      | 33/100 [00:54<01:42,  1.53s/it]
actor_val:  34%|███▍      | 34/100 [00:56<01:41,  1.54s/it]
actor_val:  35%|███▌      | 35/100 [00:58<01:45,  1.62s/it]
actor_val:  36%|███▌      | 36/100 [00:59<01:41,  1.59s/it]
actor_val:  37%|███▋      | 37/100 [01:01<01:39,  1.58s/it]
actor_val:  38%|███▊      | 38/100 [01:02<01:36,  1.56s/it]
actor_val:  39%|███▉      | 39/100 [01:04<01:34,  1.55s/it]
actor_val:  40%|████      | 40/100 [01:06<01:36,  1.62s/it]
actor_val:  41%|████      | 41/100 [01:07<01:33,  1.59s/it]
actor_val:  42%|████▏     | 42/100 [01:09<01:31,  1.57s/it]
actor_val:  43%|████▎     | 43/100 [01:10<01:33,  1.63s/it]
actor_val:  44%|████▍     | 44/100 [01:12<01:29,  1.61s/it]
actor_val:  45%|████▌     | 45/100 [01:14<01:26,  1.58s/it]
actor_val:  46%|████▌     | 46/100 [01:15<01:24,  1.57s/it]
actor_val:  47%|████▋     | 47/100 [01:17<01:31,  1.72s/it]
actor_val:  48%|████▊     | 48/100 [01:19<01:26,  1.66s/it]
actor_val:  49%|████▉     | 49/100 [01:20<01:22,  1.61s/it]
actor_val:  50%|█████     | 50/100 [01:22<01:19,  1.59s/it]
actor_val:  51%|█████     | 51/100 [01:24<01:21,  1.66s/it]
actor_val:  52%|█████▏    | 52/100 [01:25<01:21,  1.70s/it]
actor_val:  53%|█████▎    | 53/100 [01:27<01:17,  1.66s/it]
actor_val:  54%|█████▍    | 54/100 [01:28<01:15,  1.64s/it]
actor_val:  55%|█████▌    | 55/100 [01:30<01:13,  1.62s/it]
actor_val:  56%|█████▌    | 56/100 [01:32<01:10,  1.60s/it]
actor_val:  57%|█████▋    | 57/100 [01:33<01:08,  1.59s/it]
actor_val:  58%|█████▊    | 58/100 [01:35<01:06,  1.57s/it]
actor_val:  59%|█████▉    | 59/100 [01:37<01:07,  1.65s/it]
actor_val:  60%|██████    | 60/100 [01:38<01:04,  1.62s/it]
actor_val:  61%|██████    | 61/100 [01:40<01:02,  1.60s/it]
actor_val:  62%|██████▏   | 62/100 [01:41<01:02,  1.65s/it]
actor_val:  63%|██████▎   | 63/100 [01:43<01:03,  1.71s/it]
actor_val:  64%|██████▍   | 64/100 [01:45<01:02,  1.75s/it]
actor_val:  65%|██████▌   | 65/100 [01:47<00:58,  1.68s/it]
actor_val:  66%|██████▌   | 66/100 [01:48<00:55,  1.64s/it]
actor_val:  67%|██████▋   | 67/100 [01:50<00:55,  1.68s/it]
actor_val:  68%|██████▊   | 68/100 [01:51<00:52,  1.64s/it]
actor_val:  69%|██████▉   | 69/100 [01:53<00:50,  1.61s/it]
actor_val:  70%|███████   | 70/100 [01:55<00:48,  1.61s/it]
actor_val:  71%|███████   | 71/100 [01:56<00:45,  1.58s/it]
actor_val:  72%|███████▏  | 72/100 [01:58<00:44,  1.58s/it]
actor_val:  73%|███████▎  | 73/100 [02:00<00:44,  1.64s/it]
actor_val:  74%|███████▍  | 74/100 [02:01<00:42,  1.63s/it]
actor_val:  75%|███████▌  | 75/100 [02:03<00:40,  1.61s/it]
actor_val:  76%|███████▌  | 76/100 [02:04<00:38,  1.60s/it]
actor_val:  77%|███████▋  | 77/100 [02:06<00:36,  1.58s/it]
actor_val:  78%|███████▊  | 78/100 [02:07<00:34,  1.58s/it]
actor_val:  79%|███████▉  | 79/100 [02:09<00:33,  1.57s/it]
actor_val:  80%|████████  | 80/100 [02:10<00:31,  1.56s/it]
actor_val:  81%|████████  | 81/100 [02:12<00:29,  1.56s/it]
actor_val:  82%|████████▏ | 82/100 [02:14<00:27,  1.56s/it]
actor_val:  83%|████████▎ | 83/100 [02:15<00:27,  1.62s/it]
actor_val:  84%|████████▍ | 84/100 [02:17<00:25,  1.60s/it]
actor_val:  85%|████████▌ | 85/100 [02:18<00:23,  1.58s/it]
actor_val:  86%|████████▌ | 86/100 [02:20<00:21,  1.57s/it]
actor_val:  87%|████████▋ | 87/100 [02:22<00:20,  1.57s/it]
actor_val:  88%|████████▊ | 88/100 [02:23<00:19,  1.64s/it]
actor_val:  89%|████████▉ | 89/100 [02:25<00:17,  1.61s/it]
actor_val:  90%|█████████ | 90/100 [02:26<00:15,  1.58s/it]
actor_val:  91%|█████████ | 91/100 [02:28<00:14,  1.64s/it]
actor_val:  92%|█████████▏| 92/100 [02:30<00:12,  1.61s/it]
actor_val:  93%|█████████▎| 93/100 [02:31<00:11,  1.66s/it]
actor_val:  94%|█████████▍| 94/100 [02:33<00:09,  1.61s/it]
actor_val:  95%|█████████▌| 95/100 [02:35<00:07,  1.59s/it]
actor_val:  96%|█████████▌| 96/100 [02:36<00:06,  1.64s/it]
actor_val:  97%|█████████▋| 97/100 [02:38<00:04,  1.61s/it]
actor_val:  98%|█████████▊| 98/100 [02:40<00:03,  1.68s/it]
actor_val:  99%|█████████▉| 99/100 [02:41<00:01,  1.72s/it]
actor_val: 100%|██████████| 100/100 [02:43<00:00,  1.75s/it]
actor_val: 100%|██████████| 100/100 [02:43<00:00,  1.64s/it]
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 151] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 143] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 127] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 135] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 119] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 111] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 108] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 124] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 92] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 236] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 252] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 172] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 180] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 156] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 244] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 188] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 140] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 100] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 173] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 181] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 109] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 133] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 117] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 197] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 76] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 141] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 165] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 157] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 189] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 13] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 132] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 149] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 21] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 125] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 116] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 84] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 228] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 29] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 68] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 164] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 148] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 60] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 199] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 20] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 44] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 28] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 231] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 159] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 175] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 12] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 167] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 4] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 183] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 201] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 225] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 217] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 177] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 33] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 36] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 249] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 215] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 1] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 129] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 37] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 223] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 17] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 153] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 9] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 5] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 25] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 45] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 238] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 61] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 73] Training completed!
[2025-12-29 18:48:26] train_sft.py:658 - step 5000: {'val/loss': 0.571328125}
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 14] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 65] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 174] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 246] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 38] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 254] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 182] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 69] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 62] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 86] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 70] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 230] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 102] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 77] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 95] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 190] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 103] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 81] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 101] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 78] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 79] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 193] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 89] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 63] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 94] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 71] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 110] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 206] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 87] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 85] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 52] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 53] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 55] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 166] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 213] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 229] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 221] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 113] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 222] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 145] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 191] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 237] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 248] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 240] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 232] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 184] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 214] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 137] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 0] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 169] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 185] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 195] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 243] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 251] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 147] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 67] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 83] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 171] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 139] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 161] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 163] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 75] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 203] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 115] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 43] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 107] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 187] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 179] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 35] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 123] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 205] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 155] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 142] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 204] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 126] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 212] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 220] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 160] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 136] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 196] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 144] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 152] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 121] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 118] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 234] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 74] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 218] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 10] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 2] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 210] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 50] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 58] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 226] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 158] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 18] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 207] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 233] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 209] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 47] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 192] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 91] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 34] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 30] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 97] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 3] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 235] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 227] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 241] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 57] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 26] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 219] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 150] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 208] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 200] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 224] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 11] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 250] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 105] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 106] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 202] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 93] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 186] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 170] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 176] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 168] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 154] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 114] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 98] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 122] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 130] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 138] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 162] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 82] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 178] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 7] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 255] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 31] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 99] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 39] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 23] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 42] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 131] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 134] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 46] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 6] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 27] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 54] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 22] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 19] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 198] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 15] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 216] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 128] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 49] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 48] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 16] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 112] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 120] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 40] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 41] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 56] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 64] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 66] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 24] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 8] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 32] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 59] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 51] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 146] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 211] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 242] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 194] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 104] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 253] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 245] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 88] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 96] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 90] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 72] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 80] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 247] Training completed!
[2025-12-29 18:48:26] train_sft.py:729 - [Rank 239] Training completed!
wandb: 
wandb: You can sync this run to the cloud by running:
wandb: wandb sync /p/scratch/envcomp/logs/ANTA_CLAUS/wandb/offline-run-20251229_113526-fb070njd
wandb: Find logs at: ../../../../../../../scratch/envcomp/logs/ANTA_CLAUS/wandb/offline-run-20251229_113526-fb070njd/logs
[rank99]:[W1229 18:48:28.387417560 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank96]:[W1229 18:48:28.387417540 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank98]:[W1229 18:48:28.387449581 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank97]:[W1229 18:48:28.387504751 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank103]:[W1229 18:48:28.343104849 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank101]:[W1229 18:48:28.343118559 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank102]:[W1229 18:48:28.343115899 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank100]:[W1229 18:48:28.343133909 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank36]:[W1229 18:48:28.386057515 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank38]:[W1229 18:48:28.386071165 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank39]:[W1229 18:48:28.386069925 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank134]:[W1229 18:48:28.378090304 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank133]:[W1229 18:48:28.378115854 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank132]:[W1229 18:48:28.378196355 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank135]:[W1229 18:48:28.380235567 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank37]:[W1229 18:48:28.389971312 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank226]:[W1229 18:48:28.364506837 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank225]:[W1229 18:48:28.364505777 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank227]:[W1229 18:48:28.364650108 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank224]:[W1229 18:48:28.365457833 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank198]:[W1229 18:48:28.356857470 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank199]:[W1229 18:48:28.356863060 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank197]:[W1229 18:48:28.359943257 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank196]:[W1229 18:48:28.361466680 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank161]:[W1229 18:48:28.377108208 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank160]:[W1229 18:48:28.377192998 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank162]:[W1229 18:48:28.377648866 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank163]:[W1229 18:48:28.380404148 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank165]:[W1229 18:48:28.493260190 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank164]:[W1229 18:48:28.493284490 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank166]:[W1229 18:48:28.493342531 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank167]:[W1229 18:48:28.495379387 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank131]:[W1229 18:48:28.536687219 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank129]:[W1229 18:48:28.536705499 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank128]:[W1229 18:48:28.536737599 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank130]:[W1229 18:48:28.536737579 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank194]:[W1229 18:48:28.575228537 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank193]:[W1229 18:48:28.575251287 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank195]:[W1229 18:48:28.575268467 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank192]:[W1229 18:48:28.576251324 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank35]:[W1229 18:48:28.542928859 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank34]:[W1229 18:48:28.542946279 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank33]:[W1229 18:48:28.542946169 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank32]:[W1229 18:48:28.543007230 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank66]:[W1229 18:48:28.566511240 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank67]:[W1229 18:48:28.566566340 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank65]:[W1229 18:48:28.571095411 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank64]:[W1229 18:48:28.588400959 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank229]:[W1229 18:48:28.649492535 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank231]:[W1229 18:48:28.649699515 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank2]:[W1229 18:48:28.626833549 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank3]:[W1229 18:48:28.626863379 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank230]:[W1229 18:48:28.658098618 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank228]:[W1229 18:48:28.659072880 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank1]:[W1229 18:48:28.632943373 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank0]:[W1229 18:48:28.650684103 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank69]:[W1229 18:48:28.565569036 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank70]:[W1229 18:48:28.565596356 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank68]:[W1229 18:48:28.565638136 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank71]:[W1229 18:48:28.568915585 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank5]:[W1229 18:48:28.592965032 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank6]:[W1229 18:48:28.593071183 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank4]:[W1229 18:48:28.593071203 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank7]:[W1229 18:48:28.593129503 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank156]:[W1229 18:48:28.804744670 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank104]:[W1229 18:48:28.961838856 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank60]:[W1229 18:48:28.964419884 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank84]:[W1229 18:48:28.863754210 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank76]:[W1229 18:48:28.975988977 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank24]:[W1229 18:48:28.959376330 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank28]:[W1229 18:48:28.954309418 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank48]:[W1229 18:48:28.934904581 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank172]:[W1229 18:48:28.023127649 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank140]:[W1229 18:48:28.951046908 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank236]:[W1229 18:48:28.101230737 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank168]:[W1229 18:48:28.068837573 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank136]:[W1229 18:48:28.035053818 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank248]:[W1229 18:48:28.013816875 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank120]:[W1229 18:48:28.052560647 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank80]:[W1229 18:48:28.986850748 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank108]:[W1229 18:48:28.062670993 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank16]:[W1229 18:48:28.056884839 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank44]:[W1229 18:48:28.052234462 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank88]:[W1229 18:48:28.109704995 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank112]:[W1229 18:48:28.021252320 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank8]:[W1229 18:48:28.085329019 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank244]:[W1229 18:48:28.127826688 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank204]:[W1229 18:48:28.139598479 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank124]:[W1229 18:48:28.083177441 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank232]:[W1229 18:48:29.176681375 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank56]:[W1229 18:48:29.175935112 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank220]:[W1229 18:48:29.168398559 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank72]:[W1229 18:48:29.257390120 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank240]:[W1229 18:48:29.228559305 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank152]:[W1229 18:48:29.206507057 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank52]:[W1229 18:48:29.231705325 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank40]:[W1229 18:48:29.157168825 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank176]:[W1229 18:48:29.297784980 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank188]:[W1229 18:48:29.183597301 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank12]:[W1229 18:48:29.249386232 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank208]:[W1229 18:48:29.280478277 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank92]:[W1229 18:48:29.206497974 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank180]:[W1229 18:48:29.214277034 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank144]:[W1229 18:48:29.238230759 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank216]:[W1229 18:48:29.207931600 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank212]:[W1229 18:48:29.264120266 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank252]:[W1229 18:48:29.262707435 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank148]:[W1229 18:48:29.180122204 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank116]:[W1229 18:48:29.250661850 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank184]:[W1229 18:48:29.236689026 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank200]:[W1229 18:48:29.268984837 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
[rank20]:[W1229 18:48:29.916383946 ProcessGroupNCCL.cpp:1538] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())