360∘REA: Towards A Reusable Experience Accumulation with 360∘ Assessment for Multi-Agent System
Shen Gao, Hao Li, Zhengliang Shi, Chengrui Huang, Quan Tu, Shuo Shang, Zhiliang Tian, Minlie Huang · 2024
Large language model agents have demonstrated remarkable advancements across various complex tasks.Recent works focus on optimizing the agent team or employing self-reflection to iteratively solve complex tasks.Since these agents are all based on the same LLM, only conducting self-evaluation or removing underperforming agents does not substantively enhance the capability of the agents.We argue that a comprehensive evaluation and accumulating experience from evaluation feedback is an effective approach to improving system performance.In this paper, we propose Reusable Experience Accumulation with 360 • Assessment (360 • REA), a hierarchical multi-agent framework inspired by corporate organizational practices.The framework employs a novel 360 • performance assessment method for multi-perspective performance evaluation with fine-grained assessment.To enhance the capability of agents in addressing complex tasks, we introduce dual-level experience pool for agents to accumulate experience through fine-grained assessment.Extensive experiments on complex task datasets demonstrate the effectiveness of 360 • REA 1 .