PreciseCoder: Faithful and Generalizable Debugging Beyond Unit-Test Rewards
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...
| Authors | Yunkai Zhan, Miaosen Chai, Shangshang Wang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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