Alphazero-like Tree-Search can Guide Large Language Model Decoding and Training

Original Paper: https://arxiv.org/abs/2309.17179

By: Xidong Feng, Ziyu Wan, Muning Wen, Stephen Marcus McAleer, Ying Wen, Weinan Zhang, Jun Wang

Abstract:

Recent works like Tree-of-Thought (ToT) and Reasoning via Planning (RAP) aim to augment the reasoning capabilities of LLMs by using tree-search algorithms to guide multi-step reasoning. These methods rely on prompting a pre-trained model to serve as a value function and focus on problems with low search depth. As a result, these methods will not work in domains where the pre-trained LLM does not have enough knowledge to serve as an effective value function or in domains that require long-horizon planning. To address these limitations, we present an AlphaZero-like tree-search learning framework for LLMs (termed TS-LLM), systematically illustrating how tree-search with a learned value function can guide LLM decoding. TS-LLM distinguishes itself in two key ways. (1) Leveraging a learned value function and AlphaZero-like algorithms, our approach can be generally adaptable to a wide range of tasks, language models of any size, and tasks of varying search depths. (2) Our approach can guide LLMs during both inference and training, iteratively improving the LLM. Empirical results across reasoning, planning, alignment, and decision-making tasks show that TS-LLM outperforms existing approaches and can handle trees with a depth of 64.

Summary Notes

Blog Post Simplified: Boosting Large Language Models with AlphaZero-Style Tree Search

Large Language Models (LLMs) have revolutionized tasks like chatbots and text analysis due to their remarkable abilities.

However, enhancing their performance, especially in complex reasoning and decision-making, remains a challenge.

A significant advancement in this area is combining tree-search algorithms, similar to those used in AlphaZero, with LLMs. This combination, known as TS-LLM, is pushing the boundaries of what LLMs can achieve.

Introduction

Despite their success, LLMs struggle with complex multistep reasoning. The TS-LLM method, which uses deep tree-search inspired by AlphaZero, significantly improves LLMs' capabilities in handling such tasks.

Background

Previous efforts have tried to enhance LLMs' reasoning through multistep reasoning and reinforcement learning (RL) techniques. Using tree-based search methods, like Monte Carlo Tree Search (MCTS), has shown promise. TS-LLM builds on this by offering a more scalable and versatile approach.

How TS-LLM Enhances LLMs

The Approach

TS-LLM treats language generation as a sequence of decisions, using a setup where actions are token sequences, and states are the resulting texts.

The key is a reward function that evaluates performance, guiding the model to optimize outcomes.

Tree Search in Action

TS-LLM's success lies in:

• Node Expansion: Using algorithms to explore possible token sequences.
• Inference: Predicting the value of expanding certain nodes to guide the search.
• Multiple Search Strategies: Examining various paths and combining results for the best decision.

This strategy ensures a balance between exploring options and exploiting known information, significantly improving task performance.

Results

Testing TS-LLM in reasoning, planning, and decision-making tasks has shown remarkable improvements over existing methods. Its deep tree-search capabilities highlight its potential for complex language tasks.

Conclusion

TS-LLM introduces a revolutionary way to enhance LLMs, opening new possibilities for more sophisticated AI systems.

By using AlphaZero-like tree search, it significantly advances machine learning techniques in NLP.

Looking Ahead

TS-LLM marks progress in deep learning for complex reasoning and planning in NLP. It promises AI systems that better understand and interact with human language.

However, it's crucial to consider the ethical aspects of developing such technologies.

For AI engineers, especially in enterprise settings, TS-LLM presents an exciting opportunity to explore the limits of LLMs.

As we refine this framework, the vision of creating smarter, more adaptable AI systems becomes closer to reality.

This post aims to provide AI engineers with a clear understanding of the TS-LLM framework, encouraging them to explore its potential in advancing LLM capabilities.