CodeMidas scales agentic coding RL from code itself, but keep limits clear
An abstract-only preprint suggests a pipeline that turns code into RL tasks, with mixed gains and careful caveats.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the abstract
- Authors
- 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
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What the paper reports
CodeMidas creates executable RL environments from existing codebases, yielding a dataset of 5,545 training tasks across 3,185 codebases, 23 languages, and 15 domains.
Why it matters
Abstract results indicate more high-quality tasks can boost performance, but findings are limited to abstract-preprint scope and specific benchmarks.
CodeMidas builds agentic RL environments directly from implemented code, aiming to expand the range of tasks available for training coding agents.
The approach emphasizes testing against execution of original code and validating tasks through repeated solution rollouts, seeking reliable performance gains across diverse software tasks.
What this does not tell us
Abstract-only scope and preprint status; results reflect specific benchmarks and are not general population outcomes.
Original sources · 1
- CodeMidas: Scaling Agentic Coding RL Environments from Code Itself ↗arXiv · 2026-09-18
Check the original paper for its authors, methods, version and access terms.
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