Summary of Pspo*: An Effective Process-supervised Policy Optimization For Reasoning Alignment, by Jiawei Li et al.
PSPO*: An Effective Process-supervised Policy Optimization for Reasoning Alignment
by Jiawei Li, Xinyue Liang, Yizhe Yang, Chong Feng, Yang Gao
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel process supervision paradigm called PSPO*, which aims to enhance the performance of large language models in reasoning tasks by providing feedback at each step of chain-of-thought reasoning. The authors claim that the effectiveness of process supervision significantly depends on both the accuracy and the length of reasoning chains, and that these factors exhibit a nonlinear relationship with the overall reward score of the reasoning process. To address this challenge, they propose PSPO-WRS, which considers the number of reasoning steps in determining reward scores and utilizes an adjusted Weibull distribution for nonlinear reward shaping. Experimental results on six mathematical reasoning datasets demonstrate that PSPO-WRS consistently outperforms current mainstream models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how we can make large language models better at solving math problems by giving them feedback as they work through the problem step-by-step. The authors found that just providing feedback isn’t enough, and that the quality of this feedback depends on both how accurate and how long the reasoning process is. They created a new way to give feedback called PSPO-WRS, which takes into account how many steps it takes to solve the problem and uses a special kind of curve to make the reward more fair. This approach was tested on six different math problems and showed that it can perform better than current models. |
