English(EN) Two-Stage Reinforcement Learning for Sound and Adversarial Test Generation in Code LLMs

新框架使用对抗性强化学习为代码大模型生成测试用例

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-04 04:00

研究人员开发了一个名为 Test Cases Scaling (TCS) 的新颖两阶段强化学习框架，用于自动为代码生成模型生成高质量的测试用例。该框架旨在创建既可靠又具辨别力的测试，作为反例来识别求解器的故障模式。通过两阶段训练测试生成器，首先使其与参考解决方案保持一致，然后生成对抗性反例，TCS 在 TACO 和 LiveCodeBench 等基准测试中显示出 pass@1 率和推理时答案选择的改进。 AI

影响这项研究通过实现更有效的对抗性测试用例生成，有可能提高代码生成模型的评估和鲁棒性。

排序理由该集群包含一篇详细介绍代码大模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Jiacheng Xu, Wentao Zhang, Zhiyi Lyu, Fuxiang Zhang, Chaojie Wang, Yang Liu, Bo An · 2026-09-04 04:00

用于代码大语言模型声音和对抗性测试生成的两阶段强化学习

arXiv:2609.03955v1 Announce Type: new Abstract: Reinforcement learning (RL) has substantially advanced code generation with large language models (LLMs) through executable feedback. The feedback for coding problems mainly comes from specific test cases, where high-quality test ca…

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报道来源 [1]

用于代码大语言模型声音和对抗性测试生成的两阶段强化学习

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