code-critic-model

Qwen3-8B-Critic-SFT-Qwen-only

README

License: apache-2.0

Where it appears in the paper

Table with columns: Paper location, Row label
Paper location	Row label
Table 3, corpus ablation	`8B, Qwen-only`

Original run name: qwen3-8b-full-sft-prm-r2egym-swebench-instructions-k5-qwen-only-opus-distill-32k-lr5e6-multiturn.

Training data

code-critic-model/critic-sft-qwen-only, 1,915 examples.

Tasks: 483 R2E-Gym instances from matplotlib, moto, and sympy, disjoint from SWE-bench Verified.
Agent that produced the trajectories: Qwen3-Next-80B-A3B-Instruct, 483 trajectories.
Teacher: Claude Opus 4.6, queried every 5 agent steps with the high-level prompt.

This corpus is the Qwen half of critic-sft-cwm-qwen. It is smaller than the CWM half because Qwen3-Next finishes tasks in fewer steps, so fewer critique points are collected per trajectory.

Training setup

Identical to Qwen3-8B-Critic-SFT apart from the data. Full-parameter SFT with LLaMA-Factory, config finetuning/qwen3_8b_critic_full_sft_l40s_train_multiturn_resumable.yaml.

Table with columns: Setting, Value
Setting	Value
Base model	Qwen/Qwen3-8B
Chat template	`qwen3_nothink`
Sequence length	32,768 tokens
Loss	final critique turn only (`mask_history: true`)
Hardware	8 x L40S, effective batch 8
Optimizer	AdamW, lr 5e-6, cosine, warmup ratio 0.1
Epochs	3
Precision

The trainer saved this run's weights in fp32. The checkpoint here was cast to bf16 before upload, which is the precision it was served in for every result in the paper.

Results

Resolve rate on SWE-bench Verified, from Table 3 of the paper.

Table with columns: Coding agent, No critic, + this critic, + Qwen3-8B-Critic-SFT (CWM + Qwen data)
Coding agent	No critic	+ this critic	+ Qwen3-8B-Critic-SFT (CWM + Qwen data)
Qwen3-Next-80B-A3B	20.0	26.2	25.2
Qwen3-32B	8.8	10.6	13.8

Training only on Qwen3-Next trajectories gives the best result on Qwen3-Next itself and the weakest on Qwen3-32B. The mixed corpus is the better default across agents.

How to use

Same serving and launch procedure as Qwen3-8B-Critic-SFT: serve with vLLM in bf16 and pass the served name to scripts/run_critic_max150.sh with --prm. The served name must have an entry in mini-swe-agent/configs/litellm_model_registry.json; add one for this model if you use a new name.

Citation

bibtex
@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}
}

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Model Details

Model Provider

code-critic-model

Model Tree

Base

Qwen/Qwen3-8B

Fine-tuned

this model

Input Modalities

Text

Output Modalities