Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation (2311.04254v3)

Abstract: Recent advancements in 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.

• The paper introduces XoT, an innovative method that integrates reinforcement learning, MCTS, and LLMs to overcome performance, efficiency, and flexibility limitations.
• It demonstrates superior problem-solving capabilities on tasks like the Game of 24, 8-Puzzle, and Pocket Cube by reducing LLM interactions and computational costs.
• The work redefines cognitive inference by providing a comprehensive framework that optimizes thought generation through collaborative thought revision and planning.

"Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation" (2311.04254)

Introduction

The paper "Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation" presents a novel thought prompting approach, "Everything of Thoughts" (XoT), aimed at enhancing the capabilities of LLMs in decision-making and problem-solving across various domains. The authors identify a critical challenge in existing thought generation paradigms—namely, the inability to simultaneously achieve high levels of performance, efficiency, and flexibility, analogous to the "Penrose triangle."

Methodology

The introduction of XoT represents an innovative approach to defy this "Penrose triangle" limitation by leveraging reinforcement learning and Monte Carlo Tree Search (MCTS). XoT incorporates pretrained policy and value networks that perform thought searching, providing LLMs with cognitive pathways that integrate external knowledge and planning capabilities. The system autonomously generates comprehensive cognitive mappings using the MCTS-LLM collaborative thought revision framework.

XoT Architecture

XoT operates as a collaborative framework comprising two primary components: the MCTS module and an LLM solver. The MCTS module is guided by policy and value networks to conduct efficient thought exploration. Once trained, the LLM acts as a thought reviser, guided by MCTS-generated pathways, determining optimal solutions. This synergy enables effective handling of multi-solution problems and diverse thought topologies.

Experiments and Results

The authors evaluate XoT on complex problem-solving tasks such as the Game of 24, 8-Puzzle, and Pocket Cube. The results consistently demonstrate that XoT outperforms existing paradigms across various metrics. Remarkably, XoT achieves superior accuracy and efficiency with fewer LLM interactions, thereby reducing computational costs associated with thought evaluation.

Figure 1: Comparison of XoT versus other prompting paradigms.

Through experimental results, XoT asserts its position as a proficient thought generation approach. The method excels in tasks involving complex problem-solving requirements, showcasing superior capabilities in generating flexible and efficient cognitive mappings.

Figure 2: An illustration of iterative phases in MCTS for thought searching ((a)-(c)) and thought inference in problem resolution (d).

Thought Generation and MCTS Integration

XoT effectively integrates MCTS, bypassing the high inference costs of LLMs by redirecting thought evaluation to lightweight networks. This enhances the efficiency of thought generation and supports LLMs in long-term reasoning and planning—areas typically identified as limitations in LLM-only approaches.

Figure 3: An illustration of thought revision process in XoT.

The method facilitates detailed thought trajectories, optimizing the state-action sequences aligned with the problem-solving objectives. The thought revision process within XoT notably improves thought quality with minimal LLM interactions, leveraging MCTS as a robust search mechanism.

Conclusion

The introduction of XoT offers significant advancements in the domain of thought generation for LLMs, breaking free from the performance, efficiency, and flexibility limitations imposed by traditional paradigms. By integrating efficient planning models like MCTS and leveraging LLMs' cognitive abilities, XoT offers a comprehensive framework capable of addressing complex problem-solving scenarios with multi-solution capacities.

The paper not only demonstrates the competitive edge of the XoT framework through rigorous experiments but also opens avenues for future research to explore its applicability across varied tasks. The methodological innovation in blending reinforcement learning with LLMs redefines the field of AI-driven problem-solving, underscoring XoT's pivotal role in advancing cognitive inference capabilities.

Figure 4: Accuracy, LLM and $f_\theta$ invoked comparison on XoT w.r.t. the number of revisions.