Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System

Abstract

Large Language Model (LLM) based multiagent systems (MAS) show remarkable potential in collaborative problem-solving, yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods.We present OPTIMA, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness in LLM-based MAS through training.OPTIMA employs an iterative generate, rank, select, and train paradigm with a reward function balancing task performance, token efficiency, and communication readability.We explore various algorithms, including Supervised Fine-Tuning, Direct Preference Optimization, and their hybrid approaches, providing insights into their effectiveness-efficiency trade-offs.We integrate Monte Carlo Tree Search-inspired techniques for DPO data generation, treating conversation turns as tree nodes to explore diverse interaction paths.Evaluated on common multi-agent tasks, including information-asymmetric question answering and complex reasoning, OPTIMA shows consistent and substantial improvements over single-agent baselines and vanilla MAS based on Llama 3 8B / 3.2 3B, achieving up to 2.8x performance gain with less than 10% tokens on tasks requiring heavy information exchange.Moreover, OPTIMA's efficiency gains enable more effective compute utilization during inference, leading to improved inferencetime scaling laws.By addressing fundamental challenges in LLM-based MAS, OPTIMA shows the potential towards scalable, efficient, and effective MAS.Our code is available at https://github.com/thunlp/Optima.