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LLMs Over-Edit Code, New Research Finds

By PulseAugur Editorial · [3 sources] ·

A new research paper explores the issue of "over-editing" in large language models (LLMs) when they are used to repair code. The study found that even advanced models like GPT-5.5 tend to make larger edits than necessary, increasing complexity and reducing reviewability. Researchers developed a framework using BigCodeBench to evaluate this, demonstrating that a "preservation instruction" can significantly improve edit fidelity. The findings suggest that while supervised fine-tuning can overfit to specific corruption patterns, reinforcement learning offers a better trade-off for learning minimal and faithful code edits. AI

IMPACT Highlights a key limitation in current LLM code editing capabilities, suggesting areas for improvement in model training and evaluation for more precise and reviewable code repairs.

RANK_REASON The cluster contains a research paper detailing a new evaluation framework and findings on LLM code editing capabilities.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs Over-Edit Code, New Research Finds

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The cluster contains a research paper detailing a new evaluation framework and findings on LLM code editing capabilities.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Tongyao Zhu, Wei Hern Lim, Min-Yen Kan ·

    When Models Edit Too Much: On the Fidelity of Minimal Code Edits

    arXiv:2609.04061v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to edit existing code, but correctness alone is not enough: useful repairs should also be minimal, reviewable, and faithful to the original implementation. We study over-editing, …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    When Models Edit Too Much: On the Fidelity of Minimal Code Edits

    Large language models (LLMs) are increasingly used to edit existing code, but correctness alone is not enough: useful repairs should also be minimal, reviewable, and faithful to the original implementation. We study over-editing, the tendency of a model to rewrite code beyond wha…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    When Models Edit Too Much: On the Fidelity of Minimal Code Edits

    Large language models frequently over-edit code during repair, but preservation instructions and reinforcement learning can improve edit fidelity without sacrificing correctness.

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