Summary of Building Math Agents with Multi-turn Iterative Preference Learning, by Wei Xiong et al.
Building Math Agents with Multi-Turn Iterative Preference Learning
by Wei Xiong, Chengshuai Shi, Jiaming Shen, Aviv Rosenberg, Zhen Qin, Daniele Calandriello, Misha Khalman, Rishabh Joshi, Bilal Piot, Mohammad Saleh, Chi Jin, Tong Zhang, Tianqi Liu
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 a new framework for improving the problem-solving capabilities of large language models (LLMs) in mathematical reasoning tasks. By integrating external tools, such as code interpreters, and employing multi-turn Chain-of-Thought (CoT) reasoning, LLMs can solve complex math problems more effectively. The framework uses direct preference learning to optimize trajectory-level preferences and leverages feedback from code interpreters. This approach is validated through training of various language models using augmented prompts from the GSM8K and MATH datasets. The results show substantial improvements in performance, with a Gemma-1.1-it-7B model’s accuracy increasing from 77.5% to 83.9% on GSM8K and from 46.1% to 51.2% on MATH. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps LLMs solve math problems better by using special tools and thinking processes. It creates a new way for the models to learn and improve, which makes them more accurate at solving math problems. The results show that this approach can help LLMs get better at solving math problems, making it useful for things like education and research. |
