Reader · no content
No body text on file.
Open the original to read the full piece.
Recursive Agent Optimization
- Authors
- Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang...
Brief
Recursive Agent Optimization (RAO) is an RL approach for training agents that recursively spawn and delegate to new self‑instantiations, enabling an inference‑time divide‑and‑conquer scaling algorithm. The method trains policies for delegation and inter‑agent communication, and the authors report better training efficiency, the ability to solve problems beyond the model's context window, stronger generalization to harder tasks, and reduced wall‑clock time compared to single‑agent baselines. Summary is based on the paper's abstract (Apurva Gandhi et al., arXiv:2605.06639v1, 2026-05-07).
Why it matters
Recursive Agent Optimization (RAO) is an RL method that trains recursive agents which can spawn and delegate sub-tasks to new instantiations of themselves at inference time, implementing a divide‑and‑conquer/inference‑time scaling strategy.
Key details
- RAO trains agents to learn when and how to delegate and communicate; the authors report improved training efficiency and generalization to tasks much harder than those seen during training.
- The paper claims RAO enables scaling beyond a model's context window and can reduce wall‑clock time versus single‑agent systems (Apurva Gandhi et al., arXiv:2605.06639v1, published 2026-05-07).