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Self-Improving Agent Orchestrator Learns via Recursive Self-Play
What is R^2Orch: Recursive Self-Improvement for Agent Orchestration via Self-Play within a Skill-Internalization Loop?
R^2Orch introduces a loop where agents play against themselves to internalize new skills and automatically generate training curricula. This self-play framework reduces the need for hand-crafted tasks and avoids the collapse of advantage when rollouts converge.
Who it is for
Researchers and developers building multi-agent systems should watch it, as it offers a way to let agents improve without manual curriculum design.