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Cursor AI describes its recursive agent system for scaling training of its Composer model, using a fleet of agents that self-manage and alert humans when issues arise. The system enables parallel experiments and accelerates research, treating researcher time as the scarcest resource.
RAO (Recursive Agent Optimization) is an end-to-end reinforcement learning approach for training LLM agents to spawn, delegate to, and coordinate with recursive copies of themselves, turning recursive inference into a learned capability.