Datasets:

code-critic-model
/

critic-sft-cwm-only

license: apache-2.0
task_categories:
  - text-generation
language:
  - en
size_categories:
  - 1K<n<10K
tags:
  - code-agent
  - critic
  - process-reward-model
  - swe-bench
  - sft
  - llama-factory
configs:
  - config_name: default
    data_files:
      - split: train
        path: prm_sft_train.jsonl

critic-sft-cwm-only

The CWM-only SFT corpus from Steer, Don't Solve: Training Small Critic Models for Large Code Agents. It trains Qwen3-8B-Critic-SFT-CWM-only, the CWM-only arm of the corpus ablation in Table 3.

Each record is one critique point: a CWM-32B trajectory up to some step, followed by the critique that Claude Opus 4.6 wrote for it under the paper's high-level prompt. The main corpus, critic-sft-cwm-qwen, is this set plus 1,915 records from Qwen3-Next trajectories.


Records	4,532
Trajectories	500, all from CWM-32B
Tasks	500 R2E-Gym instances from matplotlib, moto, and sympy, disjoint from SWE-bench Verified
Teacher	Claude Opus 4.6 (`us.anthropic.claude-opus-4-6-v1`), queried every 5 agent steps
Teacher prompt	high-level (`prm_issue_res_instructions` in the repository)
Agent scaffold	mini-swe-agent, one bash command per step
Length limit	32,768 tokens per record

The same 500 tasks and trajectories, critiqued with the detailed prompt instead, are in critic-sft-cwm-only-detailed-prompt.

Format

One JSON object per line with a single field, messages, in ShareGPT layout: a system turn with the critic instructions, alternating user / assistant turns carrying the agent's trajectory (task and command output on the user side, agent reasoning and bash command on the assistant side), and a final assistant turn holding the teacher critique. Critiques the agent received earlier in the trajectory appear inside the user turn that follows them. The full description of the format, with an example critique, is on the critic-sft-cwm-qwen card.

Files

File	Purpose
`prm_sft_train.jsonl`	the records
`dataset_info.json`	LLaMA-Factory dataset registration (`sharegpt` format)
`metadata.json`	provenance: source run, agent, teacher, counts, instance ids

Using it

from datasets import load_dataset
ds = load_dataset("code-critic-model/critic-sft-cwm-only", split="train")

For LLaMA-Factory, point dataset_dir at a download of this repository and use dataset: prm_sft_train with the config finetuning/qwen3_8b_critic_full_sft_l40s_train_multiturn_resumable.yaml from the critic-training repository.

Citation

@misc{gandhi2026steerdontsolvetraining,
  title={Steer, Don't Solve: Training Small Critic Models for Large Code Agents},
  author={Shubham Gandhi and Yiqing Xie and Atharva Naik and Ruichen Zhu and Carolyn Rose},
  year={2026},
  eprint={2606.21811},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/2606.21811}
}