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Bibliographic Details
Main Authors: Robeyns, Maxime, Aitchison, Laurence
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2506.02211
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author Robeyns, Maxime
Aitchison, Laurence
author_facet Robeyns, Maxime
Aitchison, Laurence
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2506_02211
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Improving LLM-Generated Code Quality with GRPO
Robeyns, Maxime
Aitchison, Laurence
Artificial Intelligence
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.
title Improving LLM-Generated Code Quality with GRPO
topic Artificial Intelligence
url https://arxiv.org/abs/2506.02211