NUS Study Finds LLMs Over-Edit Code; RL Boosts Minimal-Edit Fidelity Without Losing Accuracy
NationalUniversityofSingapore · hf · 2026-09-07
Researchers from the National University of Singapore show that LLMs frequently over-edit code during repair, changing far more than the minimal patch needed to fix a bug.
The paper explores two fixes: explicit preservation instructions in the prompt, and reinforcement learning to reward minimal edits. Both improve edit fidelity without sacrificing correctness, a practical direction for reducing regressions when using AI coding assistants for local fixes.
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