Optima: Optimizing Effectiveness and Efficiency for LLM-Based
  Multi-Agent System
Large Language Model (LLM) based multi-agent 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 LLM
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 RL 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, achieving up to 2.8x performance gain with less than
10\% tokens on tasks requiring heavy information exchange. Moreover, Optima's
efficiency gains open new possibilities for leveraging inference-compute more
effectively, leading to improved inference-time scaling laws. By addressing
fundamental challenges in LLM-based MAS, Optima shows the potential towards
scalable, efficient, and effective MAS
(https://chenweize1998.github.io/optima-project-page).
67088f119e319cca02be09f2
Weize Chen
admin_assigned
2024-10-11T11:24:46.121Z
648312243b7fe59c876c0dca
/avatars/c26ad76cd213529e4670bb599b8199bb.svg
weize
user
weizechen
67088f119e319cca02be09f3
Jiarui Yuan
67088f119e319cca02be09f4
Chen Qian
67088f119e319cca02be09f5
Cheng Yang
67088f119e319cca02be09f6
Zhiyuan Liu
admin_assigned
2024-10-11T11:24:01.889Z
6472f1f7485a7c8e1bcf507b
/avatars/79dca137583875191ea883b193b630b7.svg
Zhiyuan Liu
user
zibuyu9
67088f119e319cca02be09f7
Maosong Sun
Optima: Optimizing Effectiveness and Efficiency for LLM-Based
  Multi-Agent System
Large Language Model (LLM) based multi-agent 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 LLM
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 RL 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, achieving up to 2.8x performance gain with less than
10\% tokens on tasks requiring heavy information exchange. Moreover, Optima's
efficiency gains open new possibilities for leveraging inference-compute more
effectively, leading to improved inference-time scaling laws. By addressing
fundamental challenges in LLM-based MAS, Optima shows the potential towards
scalable, efficient, and effective MAS
(https://chenweize1998.github.io/optima-project-page).
Optima framework improves communication efficiency and task performance in LLM-based multi-agent systems through iterative training with reward functions and RL algorithms, achieving substantial gains over single-agent baselines.
LLM-based multi-agent systems communication efficiency task effectiveness iterative generate rank select train paradigm reward function Supervised Fine-Tuning Direct Preference Optimization Monte Carlo Tree Search information-asymmetric question answering complex reasoning inference-compute scaling laws