CodeMidas: Scaling Agentic Coding RL Environments from Code Itself
CodeMidas turns source code into 5,545 RL tasks and improves MiMo-V2.5 on five coding benchmarks.
CodeMidas is an agentic pipeline that turns implemented functionality in open-source code into executable reinforcement-learning tasks using only source code. The dataset has 5,545 training tasks from 3,185 codebases spanning 23 languages and 15 domains. Training MiMo-V2.5 with GRPO improved all five reported benchmarks, including DeepSWE by 11.7%, ProgramBench by 17%, and Terminal-Bench v2.1 by 8.5%. Ablations show more high-quality tasks and stronger exploration and self-verification.
- 5,545 tasks drawn from 3,185 codebases and 23 languages
- Pipeline uses source code only, not issues or commits
- MiMo-V2.5 trained with GRPO improved all five benchmarks
- Gains include DeepSWE 11.7%, ProgramBench 17%, Terminal-Bench 8.5%
From the original article
1 min read at arxiv.orgTraining 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.
By Bowen Ye · arxiv.org
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