Poster
in
Workshop: Multi-Agent Systems in the Era of Foundation Models: Opportunities, Challenges and Futures
FlowReasoner: Reinforcing Query-Level Meta-Agents
Hongcheng Gao · Yue Liu · Yufei He · Longxu Dou · Chao Du · Zhijie Deng · Bryan Hooi · Min Lin · 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.