Summary of Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models, by Danqing Wang et al.
Cooperative Strategic Planning Enhances Reasoning Capabilities in Large Language Models
by Danqing Wang, Zhuorui Ye, Fei Fang, Lei Li
First submitted to arxiv on: 25 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel cooperative multi-agent reasoning framework, CoPlanner, is proposed to enhance the reasoning capabilities of large language models (LLMs) for complex, multi-step problems. The framework consists of two LLM agents: a planning agent providing high-level strategic hints and a reasoning agent inferring answers based on these hints. By training the planning agent’s policy through interactive reasoning via Proximal Policy Optimization (PPO), CoPlanner outperforms previous methods by 9.94% on LogiQA and 3.09% on BBH. The guidance from the planning agent and effective cooperation between agents contribute to superior performance in tackling multi-step reasoning problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are getting better at understanding language, but they need help solving complex problems that require multiple steps. This paper creates a new way for LLMs to work together to solve these problems. It’s like having a team of experts working together to figure out the answer. The team has two members: one that comes up with a plan and another that follows the plan to get the answer. By teaching this planning member how to make good decisions, the team can solve problems better than before. This new way of working together lets LLMs do even more complicated tasks. |
Keywords
» Artificial intelligence » Optimization
