Summary of Generative Verifiers: Reward Modeling As Next-token Prediction, by Lunjun Zhang et al.
Generative Verifiers: Reward Modeling as Next-Token Prediction
by Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, Rishabh Agarwal
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 novel approach to training verifiers for large language models (LLMs) by jointly optimizing next-token prediction and solution generation objectives. This method, called generative verifiers (GenRM), leverages the text generation capabilities of LLMs, enabling seamless integration with instruction tuning, chain-of-thought reasoning, and majority voting for improved verification. Compared to standard discriminative verifiers, GenRM achieves significant performance gains on algorithmic tasks and generalization settings. The proposed approach also shows promise in picking out subtle errors on math problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to help large language models (LLMs) make better decisions by training special “verifier” models that can understand what the LLM is saying. These verifiers are usually trained to just score solutions, but this paper shows that if we train them to generate text too, they can do many things better, like solve math problems and pick out mistakes. This new approach is called generative verifiers (GenRM) and it’s very good at making decisions and finding errors. |
Keywords
» Artificial intelligence » Generalization » Instruction tuning » Text generation » Token
