LLM Collaboration With Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2508.04652v1
• Date: Wed, 06 Aug 2025 17:18:25 GMT
• Title: LLM Collaboration With Multi-Agent Reinforcement Learning
• Authors: Shuo Liu, Zeyu Liang, Xueguang Lyu, Christopher Amato,
• Abstract summary: We develop a multi-agent, multi-turn algorithm, Multi-Agent Group Relative Policy Optimization (MAGRPO), to solve a cooperative Multi-Agent Reinforcement Learning (MARL) problem.<n>Our experiments on LLM writing and coding collaboration demonstrate that fine-tuning MAS with MAGRPO enables agents to generate high-quality responses efficiently through effective cooperation.
• Score: 13.900227188164209
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A large amount of work has been done in Multi-Agent Systems (MAS) for modeling and solving problems with multiple interacting agents. However, most LLMs are pretrained independently and not specifically optimized for coordination. Existing LLM fine-tuning frameworks rely on individual rewards, which require complex reward designs for each agent to encourage collaboration. To address these challenges, we model LLM collaboration as a cooperative Multi-Agent Reinforcement Learning (MARL) problem. We develop a multi-agent, multi-turn algorithm, Multi-Agent Group Relative Policy Optimization (MAGRPO), to solve it, building on current RL approaches for LLMs as well as MARL techniques. Our experiments on LLM writing and coding collaboration demonstrate that fine-tuning MAS with MAGRPO enables agents to generate high-quality responses efficiently through effective cooperation. Our approach opens the door to using other MARL methods for LLMs and highlights the associated challenges.

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