SOURCE-LINKED INTELLIGENCE

CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

arXiv · AI, language, vision and robotics · article · Sep 18, 2026 · UTC

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 constru

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