Summary of Towards Intrinsic Self-correction Enhancement in Monte Carlo Tree Search Boosted Reasoning Via Iterative Preference Learning, by Huchen Jiang et al.
Towards Intrinsic Self-Correction Enhancement in Monte Carlo Tree Search Boosted Reasoning via Iterative Preference Learning
by Huchen Jiang, Yangyang Ma, Chaofan Ding, Kexin Luan, Xinhan Di
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 proposes a new approach to enhancing the reasoning capabilities of Large Language Models (LLMs) through iterative self-correction. The authors leverage step-wise preference learning to improve an LLM’s ability to reason by itself, relying on its own predictions as training data. The method consists of two stages: first, the LLM enhances its self-correction abilities by predicting its own outputs; then, it applies this enhanced self-correct policy to a baseline step-wise preference learning algorithm. Experiments show that this approach outperforms existing methods in arithmetic reasoning tasks, achieving increases in accuracy on MATH and GSM8K datasets. The paper’s contributions include the development of an intrinsic self-correction mechanism for LLMs and its application to enhance step-wise preference learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to improve computers’ ability to think and reason like humans. The scientists developed a new way to train these computer models, called Large Language Models (LLMs), to make better decisions on their own. They used a two-step process: first, the LLM learned from its own predictions; then, it used this knowledge to improve its decision-making abilities. This approach was tested on math problems and showed significant improvements over existing methods. The goal is to create more intelligent computers that can solve complex problems independently. |
