不用跑代码就能判断AI修没修对
训练AI写代码时，需要判断它生成的补丁对不对。传统做法是搭Docker环境跑单元测试，成本高、速度慢。这篇论文搞了个新方法：让AI自己读仓库里的代码、文档、issue，像人类开发者一样搜集证据，然后直接判断补丁是否正确，完全不执行代码。在评测中，这个无环境验证器比最强的开源方案高出14.3个AUC点。用它来筛选训练数据和提供奖励，最终模型在SWE-bench上达到了62%的修复率，超过了需要跑环境的基线。它不是你明天就能直接用的工具，但指向一个趋势：AI写代码的验证环节，正在从“跑测试”转向“读代码”。
📄 原文摘要(英文)
Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL). Standard execution-based verification requires running unit tests inside per-repository environments such as Docker images, incurring substantial environment setup costs. We propose Dockerless, an environment-free agentic patch verifier that evaluates generated code patches without executing them. Rather than simply matching candidate patches to references, Dockerless judges patch correctness using evidence gathered through agentic repository exploration. On a verifier evaluation benchmark, Dockerless outperforms the strongest open-source verifier by 14.3 AUC points. Using Dockerless as both the SFT trajectory filter and the RL reward enables a fully environment-free post-training pipeline. The resulting model reaches 62.0%, 50.0%, and 35.2% resolve rate on SWE-bench Verified, Multilingual, and Pro, respectively. It surpasses the Qwen3.5-9B baseline by 2.4, 8.7, and 2.9 points, matching environment-based post-training.