title: FlowReasoner: Reinforcing Query-Level Meta-Agents
summary: 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.
paper_authors._id: 68070ee593d1301c2f2ade99
paper_authors.name: Hongcheng Gao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:45:06.014Z
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paper_authors.fullname: Hongcheng Gao
paper_authors.type: user
paper_authors.user: HongchengGao
paper_authors._id: 68070ee593d1301c2f2ade9a
paper_authors.name: Yue Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: yueliu1999
paper_authors.type: user
paper_authors.user: yueliu1999
paper_authors._id: 68070ee593d1301c2f2ade9b
paper_authors.name: Yufei He
paper_authors._id: 68070ee593d1301c2f2ade9c
paper_authors.name: Longxu Dou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:50:46.818Z
paper_authors._id: 6214e4ee1e35c843d42d1f88
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paper_authors.fullname: Longxu Dou
paper_authors.type: user
paper_authors.user: dreamerdeo
paper_authors._id: 68070ee593d1301c2f2ade9d
paper_authors.name: Chao Du
paper_authors._id: 68070ee593d1301c2f2ade9e
paper_authors.name: Zhijie Deng
paper_authors._id: 68070ee593d1301c2f2ade9f
paper_authors.name: Bryan Hooi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:45:53.602Z
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paper_authors.fullname: Bryan Hooi
paper_authors.type: user
paper_authors.user: bhooi
paper_authors._id: 68070ee593d1301c2f2adea0
paper_authors.name: Min Lin
paper_authors._id: 68070ee593d1301c2f2adea1
paper_authors.name: Tianyu Pang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-22T10:46:22.858Z
paper_authors._id: 63d91b6d255ef6add20e1b38
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1675921369867-63d91b6d255ef6add20e1b38.jpeg
paper_authors.fullname: Tianyu Pang
paper_authors.type: user
paper_authors.user: P2333
paper_title: FlowReasoner: Reinforcing Query-Level Meta-Agents
paper_summary: 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.
paper_ai_summary: A meta-agent named FlowReasoner automates the design of query-level multi-agent systems using DeepSeek R1 and reinforcement learning, excelling in performance, complexity, and efficiency across benchmarks.
paper_ai_keywords: DeepSeek R1 reinforcement learning multi-agent systems deliberative reasoning