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

TL;DR

PE-MA introduces a parameter-efficient framework for scalable, personalized co-evolution in multi-agent systems, reducing communication overhead while maintaining effective collaboration and adaptation.

Contribution

The paper proposes PE-MA, a novel framework enabling efficient, scalable, and personalized multi-agent co-evolution with a lightweight adapter design and optimal convergence guarantees.

Findings

Achieves asymptotically optimal convergence rate of O(1/(NK)^{1/2})

Supports agent-specific behavior with lightweight adapters

Balances global coordination and local adaptation effectively

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.