arxiv
arXiv cs.AI
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2d ago
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18 views
CodeMidas scales agentic coding RL environments using source code
Researchers introduce CodeMidas, an agentic pipeline that constructs reinforcement learning environments from implemented functionality in open-source codebases, using source code as the sole input. The method allocates agentic compute to explore functionality, construct execution-grounded tests, and validate tasks through repeated rollouts, resulting in a dataset of 5,545 training tasks across 23 programming languages. Training MiMo-V2.5 on these tasks with GRPO improves performance on five diverse benchmarks, including DeepSWE (+11.7%), ProgramBench (+17%), and Terminal-Bench v2.1 (+8.5%). Trajectory analysis indicates the RL-trained agent exhibits better behaviors such as increased codebase exploration and more diverse self-verification. These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.