Recursive Agent Optimization
Cross-source consensus on Recursive Agent Optimization from 1 sources and 5 claims.
1 sources · 5 claims
Uses
How it works
Benefits
Comparisons
Highlighted claims
- Recursive Agent Optimization trains language-model agents that can recursively spawn copies of themselves for delegated subtasks. — Recursive Agent Optimization
- RAO trains one shared policy at every node of a recursively generated execution tree. — Recursive Agent Optimization
- RAO-trained recursive agents outperformed flat single-agent baselines across the three main benchmarks. — Recursive Agent Optimization
- RAO treats recursive execution as a trainable capability rather than just an inference-time wrapper. — Recursive Agent Optimization
- RAO appears most helpful for tasks that are difficult, long-horizon, context-constrained, decomposable, or parallelizable. — Recursive Agent Optimization