Improving LLM-Generated Code Quality with GRPO

Maxime Robeyns; Laurence Aitchison

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arXiv · 2506.02211

Improving LLM-Generated Code Quality with GRPO

Maxime Robeyns·Laurence Aitchison

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Abstract

Large Language Models (LLMs) are gaining widespread use for code generation. Recent training procedures use execution feedback as a reward signal, typically focusing on the functional correctness of the code, using unit test pass rate as a reward signal. However, this reward signal fails to capture notions of maintainability, quality and safety of the code produced. We address this under-explored area and develop a comprehensive library to quantify various aspects of code quality, and use it as a reward in GRPO. We find GRPO increases code quality according to this measure, which is confirmed by expert, blinded human annotators.

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Maxime Robeyns, Laurence Aitchison. 2025-06-02. Improving LLM-Generated Code Quality with GRPO. https://arxiv.org/abs/2506.02211

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