Wenjie Gan, Zhaoxi Zhang, Jiahui Liang, Weikang Li, Wanyuan Wang, Li Zhang, Jian Dong · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27694 · open-access PDF
大型语言模型已显示出强大的代码生成能力,但它们的输出往往包含微妙的错误。自动修复此类代码需要信息化测试:暴露错误行为的输入,并与正确的预期输出相结合。最近的基于RL的测试输出方法将相同的结果奖励应用到所有推理步骤中,这可以阻止有用的推理并加强错误步骤。
Machine translation (Meta M2M100); the English original follows.
Large Language Models have shown strong code generation capabilities, yet their outputs often contain subtle bugs. Automatically repairing such code requires informative tests: inputs that expose faulty behavior, paired with correct expected outputs. Recent RL-based test generation methods apply the same outcome reward to all reasoning steps, which can discourage useful reasoning and reinforce flawed steps. They also fail to effectively learn from trajectories that find bug-triggering inputs but predict incorrect outputs, leaving their verified bug-triggering progress insufficiently exploited.
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