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arXiv · 2506.11803

PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems

Yingfan Deng·Anhao Zhou·Yuan Yuan·Xiao Zhang·Yifei Zou·Dongxiao Yu
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Abstract

Multi-Agent Systems have recently emerged as a promising paradigm for collaborative reasoning and solving complex tasks. However, the design of collaborative learning algorithms in multi-agent systems faces several challenges, including high communication overhead and insufficient agent-level personalization. In this paper, we propose PE-MA (Parameter-Efficient Multi-Agent Co-Evolution), a novel collaboration framework that supports efficient, scalable, and personalized co-evolution in multi-agent systems. In PE-MA, each agent maintains a lightweight personalized adapter to support agent-specific behavior, while a shared adapter is collaboratively optimized across neighboring agents. This design balances global coordination with local adaptation under heterogeneous environments. We achieve an asymptotically optimal convergence rate of O( 1/(NK)^(1/2) ), where N is the number of agents and K the local update steps.

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Yingfan Deng, Anhao Zhou, Yuan Yuan, Xiao Zhang, Yifei Zou, Dongxiao Yu. 2025-06-13. PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems. https://arxiv.org/abs/2506.11803

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