Recursive Agent Optimization
Apurva Gandhi · Satyaki Chakraborty · Xiangjun Wang · Aviral Kumar · Graham Neubig
We introduce Recursive Agent Optimization (RAO), a reinforcement learning algorithm for training agents that can spawn and delegate sub-tasks to new instantiations of themselves recursively.
摘要
We introduce Recursive Agent Optimization (RAO), a reinforcement learning algorithm 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 enjoy reduced wall-clock time for task completion compared to a single-agent baseline.