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 atthis https URL.
View on arXiv@article{gao2025_2504.15257,
title={ FlowReasoner: Reinforcing Query-Level Meta-Agents },
author={ Hongcheng Gao and Yue Liu and Yufei He and Longxu Dou and Chao Du and Zhijie Deng and Bryan Hooi and Min Lin and Tianyu Pang },
journal={arXiv preprint arXiv:2504.15257},
year={ 2025 }
}