Summary of Llama-berry: Pairwise Optimization For O1-like Olympiad-level Mathematical Reasoning, by Di Zhang et al.
LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning
by Di Zhang, Jianbo Wu, Jingdi Lei, Tong Che, Jiatong Li, Tong Xie, Xiaoshui Huang, Shufei Zhang, Marco Pavone, Yuqiang Li, Wanli Ouyang, Dongzhan Zhou
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 This paper introduces LLaMA-Berry, a novel framework for improving the mathematical reasoning abilities of Large Language Models (LLMs). The approach combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. By leveraging self-critic and rewriting capabilities, SR-MCTS overcomes limitations of conventional algorithms. A pairwise reward model evaluates different paths globally, while an Enhanced Borda Count method synthesizes preferences into a global ranking score. This framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability compared to existing methods like ToT and rStar. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers solve math problems better. It uses two new techniques: Monte Carlo Tree Search (MCTS) and Self-Refine. These help the computer find the best answer by trying many different solutions and choosing the best one. The computer also learns from its mistakes to improve its problem-solving skills. This approach is tested on math competitions and shows that it can solve problems more efficiently and effectively than other methods. |
Keywords
» Artificial intelligence » Llama
