用思维树+多智能体提升大模型玩陌生博弈游戏的能力
Reasoning, Memorization, and Fine-Tuning Language Models for Non-Cooperative Games
- 分四步拆解博弈求解，每步由专用语言模型代理完成
- 无需大量训练数据，仅用1000样本即达65%胜率，微调后提升至75%
- 适合想用小样本高效优化博弈策略的研究者
我们提出一种融合思维树与多智能体框架的方法，增强预训练语言模型在解决复杂陌生博弈游戏方面的能力。该方法将博弈求解分解为四个递进任务：游戏摘要、区域选择、动作提取和动作验证，分别交由特定语言模型代理处理。通过构建思维树，模拟推理路径，使代理协作提炼游戏表征与策略，缓解语言模型在推理和长期记忆上的局限。此外，采用自动化微调流程，根据对局结果（如胜负）对查询-响应对进行排序，进一步优化代理表现。该方法应用于非合作博弈场景，对抗基准算法取得65%胜率，微调后提升至75%。相比现有深度学习算法需数百万训练样本，本方法仅需约1000样本，展现出高效性与可扩展性。
原文摘要 · Abstract (English)
We develop a method that integrates the tree of thoughts and multi-agent framework to enhance the capability of pre-trained language models in solving complex, unfamiliar games. The method decomposes game-solving into four incremental tasks -- game summarization, area selection, action extraction, and action validation -- each assigned to a specific language-model agent. By constructing a tree of thoughts, the method simulates reasoning paths and allows agents to collaboratively distill game representations and tactics, mitigating the limitations of language models in reasoning and long-term memorization. Additionally, an automated fine-tuning process further optimizes the agents' performance by ranking query-response pairs based on game outcomes, e.g., winning or losing. We apply the method to a non-cooperative game and demonstrate a 65 percent winning rate against benchmark algorithms, with an additional 10 percent improvement after fine-tuning. In contrast to existing deep learning algorithms for game solving that require millions of training samples, the proposed method consumes approximately 1000 training samples, highlighting its efficiency and scalability.
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