Study Finds Multi‑Turn RL Benefits Depend on Turn Budget and Error Recovery

The study evaluates multi‑turn agentic reinforcement learning across sampling breadth K and interaction depth T using Pass@(K,T), showing that advantages appear only with sufficient turn budgets and concentrate on error recovery rather than single‑step decisions.