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arXiv AI By Mingda Zhang, Yun Huang, Qiang Huang, Yanjin Li, Rui Qian, Qichao Zhao, Tiesunlong Shen, Na Zhao, Jian Wang, Xiaoying Tang

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

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R$^2$Orch introduces a Recursive Self-Play Orchestration framework that uses an inner self‑play loop to improve an orchestrator on self‑selected tasks and an outer loop that refines how the inner loop improves. The method employs Recursive Learnability to reward task selection that yields diverse outcomes as skills are removed, and Skill Withdrawal to prune skills when the orchestrator performs equally well without them. Experiments on twelve datasets demonstrate that R$^2$Orch outperforms existing baselines in question answering, math, code, and interactive tasks.

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