FlowReasoner: Reinforcing Query-Level Meta-Agents
Authors: name: Hongcheng Gao, name: Yue Liu, name: Yufei He, name: Longxu Dou, name: Chao Du, name: Zhijie Deng, name: Bryan Hooi, name: Min Lin, name: Tianyu Pang

This paper proposes a query-level meta-agent named FlowReasoner to automate
the design of query-level multi-agent systems, i.e., one system per user query.
Our core idea is to incentivize a reasoning-based meta-agent via external
execution feedback. Concretely, by distilling DeepSeek R1, we first endow the
basic reasoning ability regarding the generation of multi-agent systems to
FlowReasoner. Then, we further enhance it via reinforcement learning (RL) with
external execution feedback. A multi-purpose reward is designed to guide the RL
training from aspects of performance, complexity, and efficiency. In this
manner, FlowReasoner is enabled to generate a personalized multi-agent system
for each user query via deliberative reasoning. Experiments on both engineering
and competition code benchmarks demonstrate the superiority of FlowReasoner.
Remarkably, it surpasses o1-mini by 10.52% accuracy across three benchmarks.
The code is available at https://github.com/sail-sg/FlowReasoner.