Recent advancements in Large Language Models (LLMs) have revolutionized decision-making by breaking down complex problems into more manageable language sequences referred to as "thoughts". An effective thought design should consider three key perspectives: performance, efficiency, and flexibility. However, existing thought can at most exhibit two of these attributes. To address these limitations, we introduce a novel thought prompting approach called "Everything of Thoughts" (XoT) to defy the law of "Penrose triangle of existing thought paradigms. XoT leverages pretrained reinforcement learning and Monte Carlo Tree Search (MCTS) to incorporate external domain knowledge into thoughts, thereby enhancing LLMs' capabilities and enabling them to generalize to unseen problems efficiently. Through the utilization of the MCTS-LLM collaborative thought revision framework, this approach autonomously produces high-quality comprehensive cognitive mappings with minimal LLM interactions. Additionally, XoT empowers LLMs to engage in unconstrained thinking, allowing for flexible cognitive mappings for problems with multiple solutions. We evaluate XoT on several challenging multi-solution problem-solving tasks, including Game of 24, 8-Puzzle, and Pocket Cube. Our results demonstrate that XoT significantly outperforms existing approaches. Notably, XoT can yield multiple solutions with just one LLM call, showcasing its remarkable proficiency in addressing complex problems across diverse domains.

翻译：大型语言模型（LLMs）的最新进展通过将复杂问题分解为更易于处理的“思维”语言序列，彻底改变了决策制定方式。有效的思维设计应兼顾三个关键维度：性能、效率与灵活性。然而，现有思维范式最多只能同时满足其中两个属性。为突破这一局限，我们提出一种名为“万事皆思维”（XoT）的新型思维提示方法，旨在挑战现有思维范式的“不可能三角”定律。XoT利用预训练强化学习与蒙特卡洛树搜索（MCTS），将外部领域知识融入思维过程，从而增强LLMs的能力，使其能高效泛化至未见问题。通过MCTS-LLM协同思维修正框架，该方法能以最少的LLM交互自动生成高质量、全面的认知映射。此外，XoT赋予LLMs无约束思考能力，允许针对多解问题灵活构建认知映射。我们在24点游戏、8数码谜题和魔方等多项具有挑战性的多解问题求解任务上评估了XoT。实验结果表明，XoT显著优于现有方法。值得注意的是，XoT单次调用LLM即可产出多种解决方案，展现了其在跨领域复杂问题处理中的卓越能力。