Summary of Generalizing Reward Modeling For Out-of-distribution Preference Learning, by Chen Jia
Generalizing Reward Modeling for Out-of-Distribution Preference Learning
by Chen Jia
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 approach to preference learning with large language models (LLMs). The authors aim to align the LLMs’ generations with human preferences, improving their generalization ability in out-of-distribution scenarios. They achieve this by optimizing a general reward model through meta-learning, which enables policy learning across various distributions. The proposed method demonstrates promising results on two text generation tasks across 20 held-out domains, outperforming several strong baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us teach computers to generate text that people like. It’s hard to get humans to give feedback on every single piece of text, so we need a way to train computer models to work well even when they don’t have much information. The researchers found a way to do this by creating a general “reward” model that can help the computers learn from small amounts of feedback. They tested their method on two types of writing tasks and showed that it works better than other approaches. |
Keywords
* Artificial intelligence * Generalization * Meta learning * Text generation
