Steer, Don't Solve: Training Small Critic Models for Large Code Agents

Abstract:Coding tasks are typically complicated and require multiple capabilities, ranging from high-level planning to low-level implementation. While coding agents are optimized for the joint capabilities, individual capabilities such as high-level planning may have different optima and remain a major bottleneck. To address this challenge, we train a separate critic model that is specialized in high-level planning to steer the coding agent in inference. We construct SFT and DPO data to train the critic model to identify errors made by the coding agent and provide correct and clear high-level guidance without generating concrete actions. Experiments show that our fine-tuned 4B and 8B critic models significantly improve the performance of 6 larger coding agents (e.g., improving the resolved rates of GLM-4.7-Flash-30B-A3B and GPT-OSS-120B by 16.0% and 14.4% on SWE-Bench Verified). The critic model also reduces the total inference costs for some coding agents by solving tasks in fewer steps (e.g., reducing the per-example inference cost for GPT-OSS-20B from \$0.07 to \$0.03). Code: this https URL

Software Engineering (cs.SE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2606.21811 [cs.SE]

(or arXiv:2606.21811v2 [cs.SE] for this version)

https://doi.org/10.48550/arXiv.2606.21811 Focus to learn more arXiv-issued DOI via DataCite

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