Summary
Recursive Agent Optimization (RAO) is a reinforcement learning approach for training recursive agents—agents that can spawn new instantiations of themselves and recursively delegate sub-tasks to them. Recursive agents implement an inference-time scaling algorithm that naturally enables divide-and-conquer behavior, allowing agents to handle longer contexts and generalize to more difficult problems. RAO trains models to best exploit this recursive inference by teaching agents when and how to delegate tasks and communicate results. According to the paper's abstract, recursively trained agents enjoy better training efficiency, can scale to tasks exceeding the model's context window, generalize to tasks much harder than those seen in training, and can achieve reduced wall-clock time compared to single-agent systems. The paper (arXiv:2605.06639) is authored by Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang, Aviral Kumar, and Graham Neubig, and was released on May 7, 2026.
Paper Overview
Research Area: NLP
Authors: Apurva Gandhi, Satyaki Chakraborty, Xiangjun Wang, Aviral Kumar, Graham Neubig
Published: 2026-05-07
arXiv: 2605.06639
Abstract (Translated)
We introduce Recursive Agent Optimization (RAO), a reinforcement learning approach for training recursive agents: agents that can spawn and delegate sub-tasks to new instantiations of themselves recursively. Recursive agents implement an inference-time scaling algorithm that naturally allows agents to scale to longer contexts and generalize to more difficult problems via divide-and-conquer. RAO provides a method to train models to best take advantage of such recursive inference, teaching agents when and how to delegate and communicate. We find that recursive agents trained in this way enjoy better training efficiency, can scale to tasks that go beyond the model's context window, generalize to tasks much harder than the ones the agent was trained on, and can enjoy reduced wall-clock time compared to single-agent systems.
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*Auto-collected on 2026-05-11*
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