Technical Report: Enhancing LLM Reasoning with Reward-guided Tree Search
Jinhao Jiang, Zhipeng Chen, Yingqian Min, Jie Chen, Xiaoxue Cheng, Jiapeng Wang, Yiru Tang, Haoxiang Sun, Jia Deng, Wayne Xin Zhao, Zheng Liu, Dong Yan, Jian Xie, Zhongyuan Wang, Ji-Rong Wen·November 18, 2024
Summary
The technical report focuses on enhancing large language models (LLMs) reasoning through reward-guided tree search. It integrates policy, reward, and search models, aiming to improve LLMs' performance in complex tasks like mathematical reasoning. The framework uses a tree search algorithm guided by a trained reward model to address the challenge of developing an o1-like reasoning approach. Extensive evaluations on four datasets demonstrate significant enhancement in reasoning abilities. The study also explores supervised format following, chain-of-thought reasoning, self-consistency, and search-based methods such as beam search and Monte Carlo Tree Search. A capable reward model, either process-based or outcome-based, guides the learning of the policy model through feedback signals. The report documents a team's preliminary exploration of a reward-guided tree search framework for improving LLM reasoning, detailing various design considerations and reporting on extensive evaluations across four mathematical benchmark datasets.
Introduction
Background
Overview of large language models (LLMs)
Challenges in enhancing LLM reasoning capabilities
Objective
Aim of the research: improving LLM performance in complex tasks
Focus on mathematical reasoning as a case study
Method
Data Collection
Types of data used for training and evaluation
Data sources and preprocessing steps
Data Preprocessing
Techniques for preparing data for the reward-guided tree search framework
Importance of data quality in enhancing LLM reasoning
Reward Model
Design and training of the reward model
Types of reward models: process-based vs. outcome-based
Policy Model
Integration of the policy model with the reward model
Learning process and feedback signals
Tree Search Algorithm
Selection and adaptation of the tree search algorithm
Role in guiding the search process
Evaluation Framework
Metrics for assessing reasoning abilities
Datasets used for evaluation
Results
Performance Improvement
Quantitative and qualitative results on four mathematical benchmark datasets
Comparison with baseline models
Exploration of Techniques
Supervised format following
Chain-of-thought reasoning
Self-consistency methods
Search-based approaches (beam search, Monte Carlo Tree Search)
Discussion
Design Considerations
Key factors influencing the effectiveness of the framework
Trade-offs and challenges encountered
Future Work
Potential improvements and extensions
Areas for further research
Conclusion
Summary of Findings
Recap of the research objectives and achievements
Implications
Impact on the field of natural language processing
Potential applications and real-world benefits
Basic info
papers
computation and language
artificial intelligence
Advanced features