R$^2$Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop

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Researchers introduced R2Orch, a recursive framework that allows AI agents to improve their own orchestration through self-play. It solves the 'advantage collapse' problem where agents get stuck when all attempts yield the same result by using a new 'Recursive Learnability' objective. Essentially, the system rewards itself for picking tasks that provide the most useful training data for its own evolution.