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Why It Matters
Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
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...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.22068v1 · Indexed about 4 hours ago