DynaSwarm: Dynamically Graph Structure Selection for LLM-based Multi-agent System

TL;DR

DynaSwarm introduces a dynamic, reinforcement learning-based framework that adaptively optimizes multi-agent collaboration graphs for improved performance across various tasks and LLM architectures.

Contribution

It presents a novel dynamic graph selection method using actor-critic RL and LLM fine-tuning, enabling sample-specific agent routing in multi-agent systems.

Findings

Outperforms state-of-the-art baselines on multiple tasks

Demonstrates the effectiveness of sample-aware graph adaptation

Shows consistent improvements across different LLMs

Abstract

Current multi-agent systems (MAS) frameworks often rely on manually designed and static collaboration graph structures, limiting adaptability and performance. To address these limitations, we propose DynaSwarm, a dynamic framework that enhances LLM-based MAS through two key innovations: (1) an actor-critic reinforcement learning (A2C) mechanism to optimize graph structures with improved stability over prior RL methods, and (2) a dynamic graph selector that adaptively chooses the optimal graph structure for each input sample via parameter-efficient LLM fine-tuning. DynaSwarm eliminates the need for rigid, one-fits-all graph architectures, instead leveraging sample-specific idiosyncrasies to dynamically route queries through specialized agent networks. (c) We propose to fine-tune the demonstration retriever to fully exploit the power of in-context learning (ICL). Extensive experiments on question answering, mathematical reasoning, and coding tasks demonstrate that DynaSwarm consistently outperforms state-of-the-art single-agent and MAS baselines across multiple LLM backbones. Our findings highlight the importance of sample-aware structural flexibility in LLM MAS designs.