Datasets:

tarsur385
/

deepswe-prm-train-embeddings-8k

Parquet error: Scan size limit exceeded: attempted to read 388230500 bytes, limit is 300000000 bytes Make sure that 1. the Parquet files contain a page index to enable random access without loading entire row groups2. otherwise use smaller row-group sizes when serializing the Parquet files

Error code:   TooBigContentError

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

DeepSWE PRM training embeddings (Qwen3-8B, 8k)

Frozen Qwen3-8B last-token-pooled embeddings (4096-d, float16) of every step of the DeepSWE training-pool rollouts: 405,919 steps · 4,701 trajectories · 113 tasks. This is the data the released DeepSWE PRM heads (tarsur385/deepswe-prm-heads-8k) were fine-tuned on. Embedded with preprocessing/deepswe/embed_shard.py at max_model_len 8192: the state is the chat-templated step context truncated to its last 8191 tokens; the action is the raw action text.

column	description
`trajectory_id`	rollout id
`step_idx`	step order within the rollout (rows are in step order)
`task_id`	DeepSWE task
`reward`	rollout outcome (1 = passed)
`model`, `config`	policy model / rollout config
`state_embedding`, `action_embedding`	4096-d float16

Load into the repository's embedding-dir format and fine-tune:

python preprocessing/hf_embeddings.py download tarsur385/deepswe-prm-train-embeddings-8k --out data/deepswe_train
python train/finetune.py --task prm --emb-dir data/deepswe_train --init-ckpt qwen3_8b_midtrained_head.pt \
    --holdout-folds heads/folds/fold0.json heads/folds/fold1.json heads/folds/fold2.json --out-dir runs/deepswe

The evaluation rollouts (Claude-Opus-5, 4 per task) are a separate dataset: tarsur385/deepswe-prm-embeddings-8k.

Downloads last month: 45