CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
Bowen Ye, Lei Li, Shicheng Li, Zihao Yue, Linghao Zhang, Hanglong Lv, Yuanxin Liu, Wenhan Ma, Hao Tian, Rang Li, Jinhao Dong, Yikai Zhao, Xiangwei Deng, Hailin Zhang, Liang Zhao, Qi Liu, Lingpeng Kong, Tong Yang, Fuli Luo
Abstract
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existing methods typically rely on development artifacts such as issues and commits, limiting the range of tasks that can be extracted. To better scale RL environments, we present CodeMidas, an agentic pipeline that turns implemented functionality in existing codebases into executable RL environments using source code as its only task-specific input. CodeMidas allocates agentic compute to every stage of environment construction: agents explore implemented functionality to formulate behavioral specifications, construct tests grounded in execution of the original code, and validate and filter candidate tasks through execution checks and repeated solution rollouts. The resulting dataset has 5,545 training tasks from 3,185 open-source codebases spanning 23 programming languages and 15 technical domains. Training MiMo-V2.5 on these tasks with GRPO improves performance on all five diverse benchmarks, covering issue repair (DeepSWE + 11.7%), whole-program construction (ProgramBench +17%), and terminal work (Terminal-B
中文速览
编码智能体的强化学习缺少大量既多样又能可靠判分的任务，而现有方法主要依赖 issue、提交记录或文档，覆盖有限。CodeMidas 只使用源代码，让多个智能体先找出可观察的已有功能并改写成待实现任务，再依据原程序运行结果生成测试，同时通过容器复测、对抗性尝试和多轮解题筛掉泄漏、误判或质量不稳定的任务。最终它从 3,185 个开源代码库中构建了 5,545 个跨 23 种语言和 15 个领域的任务，用这些任务训练 MiMo-V2.5 后，五个外部基准全都有提升，例如 DeepSWE 提升 11.7%、ProgramBench 提升 17%、Terminal-Bench 提升 8.5%。这说明源代码本身就能规模化变成带可靠验证器的强化学习环境，不仅扩大了训练数据，还能让编码智能体在修复问题、从头写程序和使用终端等不同软件任务上获得可迁移的能力。
原文 arXiv:2609.22068;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2609.22068v1