Summary of Interpretable Contrastive Monte Carlo Tree Search Reasoning, by Zitian Gao et al.
Interpretable Contrastive Monte Carlo Tree Search Reasoning
by Zitian Gao, Boye Niu, Xuzheng He, Haotian Xu, Hongzhang Liu, Aiwei Liu, Xuming Hu, Lijie Wen
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed SC-MCTS* algorithm significantly improves both reasoning accuracy and speed for Large Language Models (LLMs). The motivation behind this work comes from the limitations of previous MCTS LLM reasoning methods, which often overlooked their slow speed compared to CoT. Previous research mainly used MCTS as a tool for LLM reasoning on various tasks with limited quantitative analysis or ablation studies of its components. SC-MCTS* addresses these limitations by conducting extensive ablation studies and quantitative analysis on the components of MCTS, revealing the impact of each component on the MCTS reasoning performance of LLMs. The algorithm is designed to improve the reward model based on the principle of contrastive decoding, achieving an average speed improvement of 51.9% per node using speculative decoding. Additionally, SC-MCTS* improves the UCT node selection strategy and backpropagation used in previous works, resulting in significant performance improvements. The algorithm outperforms o1-mini by an average of 17.4% on the Blocksworld multi-step reasoning dataset using Llama-3.1-70B with SC-MCTS*. The code is available at this URL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new AI system that can reason better and faster than previous systems. They wanted to fix some problems with how AI systems reason, which was slowing them down compared to other methods. To do this, they studied the different parts of their reasoning system and found ways to improve each one. This led to big improvements in both how well the system reasoned and how fast it could do so. |
Keywords
» Artificial intelligence » Backpropagation » Llama
