Definition
A training method that teaches an AI agent to spawn copies of itself for sub-tasks and learn from the whole tree of attempts.
Recursive Agent Optimization, a training framework in which a single shared policy learns to recursively delegate sub-tasks to child agent instances, with per-node local rewards combining own-task success with average child success.
Also called: Recursive Agent Optimization