Papers › Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search

Strategist: Learning Strategic Skills by LLMs via Bi-Level Tree Search

20 Aug 2024arXiv:2408.10635archive 2025-07-28

Jonathan Light, Min Cai, Weiqin Chen, Guanzhi Wang, Xiusi Chen, Wei Cheng, Yisong Yue, Ziniu Hu

In this paper, we propose a new method STRATEGIST that utilizes LLMs to acquire new skills for playing multi-agent games through a self-improvement process. Our method gathers quality feedback through self-play simulations with Monte Carlo tree search and LLM-based reflection, which can then be used to learn high-level strategic skills such as how to evaluate states that guide the low-level execution. We showcase how our method can be used in both action planning and dialogue generation in the context of games, achieving good performance on both tasks. Specifically, we demonstrate that our method can help train agents with better performance than both traditional reinforcement learning-based approaches and other LLM-based skill learning approaches in games including the Game of Pure Strategy (GOPS) and The Resistance: Avalon. STRATEGIST helps bridge the gap between foundation models and symbolic decision-making methods through its bi-level approach, leading to more robust decision-making.

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