Summary of Process Reward Model with Q-value Rankings, by Wendi Li et al.
Process Reward Model with Q-Value Rankings
by Wendi Li, Yixuan Li
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Process Q-value Model (PQM), a novel framework for Process Reward Modeling (PRM) that optimizes Q-value rankings using a comparative loss function. This approach addresses limitations in existing PRM methods, which frame complex reasoning tasks as classification problems and employ cross-entropy loss to evaluate each step’s correctness independently. PQM captures the intricate dynamics among sequential decisions by redefining PRM within the context of a Markov Decision Process. The paper presents extensive empirical evaluations across various sampling policies, language model backbones, and multi-step reasoning benchmarks, demonstrating PQM’s superiority over classification-based PRMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The authors developed a new way to help machines learn from rewards in complex decision-making tasks. They introduced a framework called Process Q-value Model (PQM) that improves how we give rewards for each step in the process. Current methods don’t consider how individual steps are connected, which can lead to poor decisions. PQM takes this into account by treating the process as a game-like scenario where each step’s value is compared to others. The authors tested their approach and found it outperformed other methods. |
Keywords
» Artificial intelligence » Classification » Cross entropy » Language model » Loss function
