Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models
This work addresses the challenge of improving reasoning in LLMs for multi-step problems, which is incremental as it builds on existing multi-agent frameworks by enhancing cooperation.
The paper tackles the problem of enhancing reasoning capabilities in large language models for complex, multi-step tasks by proposing CoPlanner, a cooperative multi-agent framework that separates planning and reasoning duties, resulting in performance improvements of 9.94% on LogiQA and 3.09% on BBH compared to previous methods.
Enhancing the reasoning capabilities of large language models (LLMs) is crucial for enabling them to tackle complex, multi-step problems. Multi-agent frameworks have shown great potential in enhancing LLMs' reasoning capabilities. However, the lack of effective cooperation between LLM agents hinders their performance, especially for multi-step reasoning tasks. This paper proposes a novel cooperative multi-agent reasoning framework (CoPlanner) by separating reasoning steps and assigning distinct duties to different agents. CoPlanner consists of two LLM agents: a planning agent and a reasoning agent. The planning agent provides high-level strategic hints, while the reasoning agent follows these hints and infers answers. By training the planning agent's policy through the interactive reasoning process via Proximal Policy Optimization (PPO), the LLaMA-3-8B-based CoPlanner outperforms the previous best method by 9.94\% on LogiQA and 3.09\% on BBH. Our results demonstrate that the guidance from the planning agent and the effective cooperation between the agents contribute to the superior performance of CoPlanner in tackling multi-step reasoning problems.