FlowReasoner: Reinforcing Query-Level Meta-Agents
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.
68070ee593d1301c2f2ade99
Hongcheng Gao
admin_assigned
2025-04-22T10:45:06.014Z
62728f4f6253fe2068da1021
https://cdn-avatars.huggingface.co/v1/production/uploads/62728f4f6253fe2068da1021/KZ65X0EH98AF3zXemPiap.jpeg
Hongcheng Gao
user
HongchengGao
68070ee593d1301c2f2ade9a
Yue Liu
admin_assigned
2025-04-22T10:44:41.556Z
6650c77a74664a42ddfb9187
/avatars/92001bbe0ae9b14309730316b639cede.svg
yueliu1999
user
yueliu1999
68070ee593d1301c2f2ade9b
Yufei He
68070ee593d1301c2f2ade9c
Longxu Dou
claimed_verified
2025-04-22T09:50:46.818Z
6214e4ee1e35c843d42d1f88
https://cdn-avatars.huggingface.co/v1/production/uploads/6214e4ee1e35c843d42d1f88/fj-9wuIdPhvogh3BrcXTB.jpeg
Longxu Dou
user
dreamerdeo
68070ee593d1301c2f2ade9d
Chao Du
68070ee593d1301c2f2ade9e
Zhijie Deng
68070ee593d1301c2f2ade9f
Bryan Hooi
admin_assigned
2025-04-22T10:45:53.602Z
651d8032c50012d33e914f2f
/avatars/0a44c9f51fc50ce86582e328c361ea00.svg
Bryan Hooi
user
bhooi
68070ee593d1301c2f2adea0
Min Lin
68070ee593d1301c2f2adea1
Tianyu Pang
admin_assigned
2025-04-22T10:46:22.858Z
63d91b6d255ef6add20e1b38
https://cdn-avatars.huggingface.co/v1/production/uploads/1675921369867-63d91b6d255ef6add20e1b38.jpeg
Tianyu Pang
user
P2333
FlowReasoner: Reinforcing Query-Level Meta-Agents
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.
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.
DeepSeek R1 reinforcement learning multi-agent systems deliberative reasoning