ReTime / OPTR — four-setting handoff

This package contains the server-authoritative Florence–Bridge OPTR code, its complete 20K common-start state and Uniform/Residual-OPTR 20.5K evaluation snapshots. It also contains the complete Qwen3-VL-4B–Bridge 29K common-start state and the environment/seed contract needed for paired multi-seed continuation experiments.

Unified checkpoint layout

The release is being normalized under settings/. Each of the four settings has the same entries: common_start, uniform, residual, keyframe, frameskip, loss_is, configs, and dependencies.

• settings/florence_bridge/
• settings/florence_robotwin/
• settings/qwen_bridge/
• settings/qwen_robotwin/

The common-start entry is the resumable branch point. Method endpoints are evaluation-ready model snapshots. Shared source code is kept once at repository root and referenced by each setting to avoid four inconsistent copies.

Rebuttal 训练与测评种子

设置	Common-start 训练 seed	Continuation 训练 seed	Evaluation policy seeds
Florence–Bridge 20K → 20.5K	42	主实验 117465，已报告三组 117465, 533389, 944964	518957, 765353, 686765, 516408
Qwen3-VL-4B–Bridge 29K 与 29.5K	42	历史 +500 配置为 42，继承 29K RNG。新多种子清单待定	518957, 765353, 686765, 516408

完整的 Florence RNG 字段、Rebuttal 复用规则和原始记录链接见 Bridge 训练与测评种子总表。

What is included

• real_robot/：Xtrainer2 真机数据转换、训练、双臂动作适配和 policy server 源码，以及中文交接说明。
• common_start_20k/checkpoints/steps_20000_accelerate/: full resumable state (model, optimizer, scheduler metadata and per-rank RNG state). Start every new continuation arm here.
• checkpoints/uniform/steps_20500_pytorch_model.pt: model-only Uniform +500 snapshot.
• checkpoints/residual/steps_20500_pytorch_model.pt: model-only Residual-OPTR +500 snapshot.
• proposals/residual_temporal_proposal_k8_alpha0.2.json: frozen K=8, alpha=0.2 Residual proposal.
• manifests/: Bridge/RT1 provenance, content fingerprints and valid-anchor identity.
• configs/, scripts/, starVLA/: portable training and evaluation code.
• EXPERIMENT_CONTRACT.md: exact experimental contract and results.
• AGENTS.md: fast handoff instructions for an AI coding agent.
• qwen_bridge_common_start_29k/checkpoints/steps_29000_pytorch_model.pt: Qwen3-VL-4B–Bridge 29K model parameters.
• qwen_bridge_common_start_29k/checkpoints/steps_29000_training_state.pt: complete optimizer, scheduler, Python/NumPy/PyTorch/CUDA RNG and data-order state.
• qwen_bridge_common_start_29k/config.full.yaml: server-authoritative configuration that produced the 29K common start.
• qwen_bridge_common_start_29k/ENVIRONMENT.md: exact Qwen software and training environment.
• qwen_bridge_common_start_29k/MULTISEED_PROTOCOL.md: strict separation of common-start, continuation-training and evaluation seeds.
• qwen_bridge_common_start_29k/AGENTS.md: Qwen handoff constraints for an AI coding agent.

The 20.5K files are sufficient for evaluation but not for exact optimizer/RNG continuation. Any new branch must resume from the complete 20K state.

For Qwen–Bridge, exact continuation requires both 29K .pt files.

This release does not use implementation, code-path, absolute-path, current-manifest or self-referential SHA gates. Experimental identity is enforced through loadability and semantic model/data/episode/anchor/support/batch/budget checks.

Florence–Bridge headline result

Fixed no-video SimplerEnv WidowX/Bridge panel: four policy seeds (518957, 765353, 686765, 516408) × four tasks × 24 episodes = 384 rollouts.

Method	Stack	Carrot	Spoon	Eggplant	Overall
Uniform +500	13/96	31/96	50/96	68/96	162/384 = 42.19%
Residual-OPTR +500	24/96	45/96	83/96	76/96	228/384 = 59.38%

Florence–Bridge quick start

1. Download this repository and install requirements.txt in the same software environment used for StarVLA.
2. Download Florence-2-large and the OXE LeRobot Bridge/RT1 data.
3. Point the environment variables listed in AGENTS.md to those local assets.
4. Keep the contract fixed: two GPUs, BF16, ZeRO-2, micro-batch 4/GPU, accumulation 8, global batch 64, continuation seed 117465.
5. Run one arm:

bash scripts/run_florence_bridge_arm.sh uniform uniform-reproduction
bash scripts/run_florence_bridge_arm.sh residual residual-reproduction \
  proposals/residual_temporal_proposal_k8_alpha0.2.json

The launch path performs only file-presence and semantic data/support checks.

Important implementation facts

• framework.name: QwenGR00T is a historical registry name; the base VLM is Florence-2-large.
• The actual Florence–Bridge run used cross_attention_dim: 1024 and DeepSpeed ZeRO-2.
• The optimizer groups are the VLM interface and action model. No explicit freeze list was configured; the executed action path updated the Florence vision/text interface and action head, while decoder parameters outside that path did not receive updates.
• Residual sampling uses ordinary p/q objective correction. This release intentionally does not upload Matched-Random/shuffle assets.

See SOURCE_INVENTORY.md before changing any method or training field.