PreciseCoder: Augmenting Unit-Test RL with Localized Semantic Rewards Cuts Code Edits from 7–15 Lines to 3 and Boosts GPT-5.6 Sol Precision
While reinforcement learning from unit tests raises code pass rates, researchers discover it inadvertently teaches models to over-edit and hallucinate bugs in already-correct code. PreciseCoder augments unit-test rewards with a semantically verified localized edit signal. Across 4B, 9B, and 27B models, PreciseCoder slashes average edit lines from 7–15 down to 3 at equal pass rates, generalizes from single-line training to repository-level repair, and transfers 9B reasoning traces to boost GPT-5.6 Sol and Qwen3.8-Max edit precision by 4.6–12.4 points without retraining.
Key Takeaways
- Exposes unit-test RL side effects: shows unit-test-only rewards teach coding models to over-edit and hallucinate bugs in working code
- Slashes edit footprint from 7–15 lines to 3: augments unit tests with semantically verified localized edit rewards across 4B, 9B, and 27B models
- Weak-to-strong trace transfer: conditioning GPT-5.6 Sol and Qwen3.8-Max on 9B PreciseCoder reasoning traces boosts edit precision by 4.6–12.4 points zero-shot