Summary of Monte Carlo Tree Search Boosts Reasoning Via Iterative Preference Learning, by Yuxi Xie et al.
Monte Carlo Tree Search Boosts Reasoning via Iterative Preference Learning
by Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, Michael Shieh
First submitted to arxiv on: 1 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 research introduces an innovative approach to enhance the reasoning capabilities of Large Language Models (LLMs) through an iterative preference learning process inspired by AlphaZero. The method leverages Monte Carlo Tree Search (MCTS) to collect preference data, breaking down instance-level rewards into step-level signals. To ensure consistency, it combines outcome validation and stepwise self-evaluation, updating the quality assessment of newly generated data. The algorithm employs Direct Preference Optimization (DPO) to update the LLM policy using this step-level preference data. Theoretical analysis highlights the importance of on-policy sampled data for successful self-improvement. Extensive evaluations demonstrate remarkable performance improvements over existing models on arithmetic and commonsense reasoning tasks, outperforming baselines by substantial margins. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps Large Language Models (LLMs) think better by using a new way to learn from what’s good or bad. It takes inspiration from AlphaZero and uses something called Monte Carlo Tree Search to make the LLM smarter. The method checks its work and makes sure it’s doing well at each step, then updates itself to do even better. This helps the LLM get really good at things like math problems and understanding everyday language. |
Keywords
» Artificial intelligence » Optimization
