Summary of Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths, by Yew Ken Chia et al.
Reasoning Paths Optimization: Learning to Reason and Explore From Diverse Paths
by Yew Ken Chia, Guizhen Chen, Weiwen Xu, Luu Anh Tuan, Soujanya Poria, Lidong Bing
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed Reasoning Paths Optimization (RPO) framework enhances the problem-solving capabilities of advanced language models like OpenAI o1. RPO learns to reason and explore from diverse paths, encouraging favorable branches at each step while penalizing unfavorable ones. This approach improves the overall performance on multi-step reasoning tasks, such as math word problems and science-based exam questions. Experimental results demonstrate up to 3.1% and 4.3% improvement on GSM8K and MMLU (STEM) respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to train language models so they can solve problems more effectively. This is called Reasoning Paths Optimization (RPO). RPO helps the model learn how to think step-by-step, making it better at solving math word problems and science questions. The results show that this approach works well, improving the model’s performance by a significant amount. |
Keywords
* Artificial intelligence * Optimization
