Anchored Self-Play for Code Repair
Code repair is an important capability for language models (LMs): given a buggy program and unit tests, an LM must produce a fixed program that passes the tests. Because code repair data is limited, we aim to scale supervision by using an LM to generate bug--fix tasks. We propose __generator--fixer self-play__, in which a single model is trained with reinforcement learning to generate bugs and fix them. As the fixer improves, the generator adapts to produce more difficult bugs, yielding an automatic curriculum. To test whether this curriculum generalizes, we introduce BugSourceBench, a repair benchmark spanning realistic bug sources: bugs in human-written code, LM-generated code, and huma...
| Authors | Caroline Choi, Zeyneb Kaya, Shirley Wu et al. |
|---|---|
| Primary category | cs.SE |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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