Technical Report: Enhancing LLM Reasoning with Reward-guided Tree Search (2411.11694v2)

Abstract: Recently, test-time scaling has garnered significant attention from the research community, largely due to the substantial advancements of the o1 model released by OpenAI. By allocating more computational resources during the inference phase, LLMs~(LLMs) can extensively explore the solution space by generating more thought tokens or diverse solutions, thereby producing more accurate responses. However, developing an o1-like reasoning approach is challenging, and researchers have been making various attempts to advance this open area of research. In this paper, we present a preliminary exploration into enhancing the reasoning abilities of LLMs through reward-guided tree search algorithms. This framework is implemented by integrating the policy model, reward model, and search algorithm. It is primarily constructed around a tree search algorithm, where the policy model navigates a dynamically expanding tree guided by a specially trained reward model. We thoroughly explore various design considerations necessary for implementing this framework and provide a detailed report of the technical aspects. To assess the effectiveness of our approach, we focus on mathematical reasoning tasks and conduct extensive evaluations on four challenging datasets, significantly enhancing the reasoning abilities of LLMs.

Enhancing LLMs' Reasoning with Reward-guided Tree Search: An Analysis

The paper presents a comprehensive examination of a reward-guided tree search framework designed to enhance the reasoning capabilities of LLMs. This exploration centers around integrating policy and reward models with a sophisticated search algorithm to improve performance on mathematical reasoning tasks. Below, we delve into the key aspects of the research, shedding light on the implications and potential future developments in AI.

Framework Design

The proposed framework focuses on mathematical reasoning, a domain where traditional LLMs encounter significant challenges due to the complexity and depth of logical operations required. The authors introduce a three-component system comprising a policy model, a search algorithm, and a reward model. This integration aims to dynamically explore reasoning paths within an expanded solution space, guided by a reward model that provides feedback to optimize the policy model's decision-making process.

1. Policy Model: The policy model undergoes significant adaptations, including reasoning format instruction tuning, which enhances its proficiency in stepwise problem-solving aligned with the tree search structure. Preference optimization, driven by feedback from the reward model, further refines the policy's capabilities.
2. Reward Model: With a focus on generative, outcome-supervised, and scoring-based configurations, the reward model acts as an evaluator of reasoning paths. Its training involves generative modeling and active learning techniques to select high-quality data samples, ensuring accurate and effective feedback for policy model training.
3. Search Algorithm: The search algorithm employs tree-based strategies, including Monte Carlo Tree Search (MCTS) and its variations, to explore potential solutions. The framework leverages methods such as pre-expansion and self-consistency checks to optimize search efficiency and efficacy.

Evaluation and Results

The framework's performance was rigorously tested on multiple challenging mathematical benchmarks. Compared to baseline methods, such as zero-shot CoT, self-consistency, and simple best-of-N selection, the proposed framework demonstrated notable improvements in reasoning capabilities, as evidenced by its superior performance across diverse datasets.

Implications and Future Directions

The integration of reward-guided tree search presents promising implications for advancing LLMs' reasoning abilities. The enhancements in reasoning align with demands for more robust AI systems capable of handling complex logical tasks, including those found in STEM disciplines. The proposed method's ability to effectively leverage computational resources during inference suggests a pathway toward more practical, real-time applications of LLMs in dynamic environments, potentially influencing fields such as education, programming, and scientific research.

Going forward, artificial intelligence research could benefit from further exploration of scalable and efficient training and inference techniques that maintain or even enhance model performance. Additionally, expanding the framework to encompass broader domains beyond mathematical reasoning could provide insights into the generalization potential of reward-guided search methods. As AI continues to evolve, the development of more nuanced reasoning systems, rooted in intricate algorithmic frameworks like the one described, will be essential to addressing increasingly sophisticated problem-solving tasks.