PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards

Hugging Face Daily Papers Papers
reinforcement-learning code-repair debugging edit-precision llm-training generalization

Summary

PreciseCoder 提出在单元测试奖励之外加入语义验证的局部编辑奖励信号，显著减少模型在调试时的多余改动并防止幻觉式 bug 修改；在 4B/9B/27B 模型上将平均编辑行数从 7-15 行降至 3 行，并能泛化到仓库级程序修复，其推理轨迹还能提升 GPT-5.6 Sol 等前沿模型的编辑精度。

Reinforcement learning (RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this unit-test-only reward teaches a model during debugging and find that, while it raises pass rates, it also teaches the model to edit more and to hallucinate bugs in code that is already correct. We introduce PreciseCoder, a training algorithm that augments the unit-test reward with a signal for semantically verified, localized edits, which correlates with smaller edits and higher pass rates. On 4B, 9B, and 27B models, PreciseCoder reduces the average number of edit lines from 7-15to 3 at a pass rate on par with unit-test-only training. Trained only on single-line bugs, PreciseCoder can generalize to repository-level program repair, where it resolves more issues with smaller patches. Its precision also transfers from weaker to stronger models: given the reasoning trace of a 9B PreciseCoder model, frontier models such as GPT-5.6 Sol and Qwen3.8-Max gain 4.6-12.4 points in edit precision with no additional training. Linguistic analysis of the thinking traces shows that our reward encourages the model to reason about individual lines, name the buggy ones, and form consistent bug hypotheses.
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Source: https://huggingface.co/papers/2610.18755

Abstract

PreciseCoder improves code repair by rewarding precise, localized edits rather than only unit-test passage, reducing unnecessary changes and improving generalization across model sizes.

Reinforcement learning(RL) from unit tests is the common recipe for post-training code models, but a unit test only asks whether the final program passes. We analyze what this unit-test-only reward teaches a model duringdebuggingand find that, while it raises pass rates, it also teaches the model to edit more and tohallucinate bugsin code that is already correct. We introducePreciseCoder, a training algorithm that augments theunit-test rewardwith a signal for semantically verified, localized edits, which correlates with smaller edits and higher pass rates. On 4B, 9B, and 27B models,PreciseCoderreduces the average number ofedit linesfrom 7-15to 3 at a pass rate on par with unit-test-only training. Trained only on single-line bugs,PreciseCodercan generalize torepository-level program repair, where it resolves more issues with smaller patches. Its precision also transfers from weaker to stronger models: given thereasoning traceof a 9BPreciseCodermodel, frontier models such as GPT-5.6 Sol and Qwen3.8-Max gain 4.6-12.4 points inedit precisionwith no additional training. Linguistic analysis of the thinking traces shows that our reward encourages the model to reason about individual lines, name the buggy ones, and form consistent bug hypotheses.

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#### PreciseCoder/PreciseCoder-27B Reinforcement Learning• 27B• Updated2 days ago • 56 • 1 #### PreciseCoder/PreciseCoder-4B Reinforcement Learning• 5B• Updated2 days ago • 17 • 1 #### PreciseCoder/PreciseCoder-9B Reinforcement Learning• 9B• Updated2 days ago • 25 • 1

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#### PreciseCoder/pdb-test Viewer• Updated2 days ago • 1.21k • 311

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UPDATE: "Gentle Coding" is mathematically proven. 1,500+ test runs show major gain for Kimi K2.6 and even more for GLM-5.1! GPT 5.4/5.5 and Claude Sonnet 3.5/Opus 4.6 also better, with ZERO REGRESSION ACROSS THE BOARD.

Reddit r/LocalLLaMA

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