Summary of Accessing Gpt-4 Level Mathematical Olympiad Solutions Via Monte Carlo Tree Self-refine with Llama-3 8b, by Di Zhang et al.
Accessing GPT-4 level Mathematical Olympiad Solutions via Monte Carlo Tree Self-refine with LLaMa-3 8B
by Di Zhang, Xiaoshui Huang, Dongzhan Zhou, Yuqiang Li, Wanli Ouyang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces MCT Self-Refine (MCTSr), an algorithm that combines Large Language Models (LLMs) with Monte Carlo Tree Search (MCTS) to improve mathematical reasoning performance. By integrating systematic exploration, self-refinement, and evaluation mechanisms, MCTSr enhances decision-making frameworks within LLMs. The algorithm iteratively constructs a search tree using Selection, self-refine, self-evaluation, and Backpropagation, leveraging an improved Upper Confidence Bound (UCB) formula to balance exploration and exploitation. Experimental results demonstrate MCTSr’s effectiveness in solving Olympiad-level mathematical problems, significantly improving success rates across multiple datasets, including GSM8K, GSM Hard, MATH, Math Odyssey, AIME, and OlympiadBench. This study advances the application of LLMs in complex reasoning tasks and sets a foundation for future AI integration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to help computers solve hard math problems by combining two important technologies: Large Language Models (LLMs) and Monte Carlo Tree Search (MCTS). The new algorithm, called MCT Self-Refine (MCTSr), makes decisions better by exploring different options and learning from its mistakes. The results show that MCTSr is really good at solving hard math problems, even beating previous best scores on many datasets. This could be important for making computers smarter and more reliable in the future. |
Keywords
» Artificial intelligence » Backpropagation
