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.
@article{arxiv.2506.11803,
title = {PE-MA: Parameter-Efficient Co-Evolution of Multi-Agent Systems},
author = {Yingfan Deng and Anhao Zhou and Yuan Yuan and Xiao Zhang and Yifei Zou and Dongxiao Yu},
journal= {arXiv preprint arXiv:2506.11803},
year = {2025}
}