Summary of Understanding Reference Policies in Direct Preference Optimization, by Yixin Liu et al.
Understanding Reference Policies in Direct Preference Optimization
by Yixin Liu, Pengfei Liu, Arman Cohan
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 investigates the dependency on reference models in Direct Preference Optimization (DPO), a widely used method for fine-tuning large language models. The authors explore three related research questions: the optimal strength of the KL divergence constraint, the necessity of the constraint from the reference policy, and whether DPO benefits from stronger reference policies. They find that DPO is sensitive to the strength of the constraint, superior to other learning objectives in a controlled setting, and benefits from similar strong reference policies. The findings highlight the confounding role of reference policies in DPO and offer insights for best practices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how fine-tuning language models works. It’s called Direct Preference Optimization (DPO). The researchers want to know if the way we use DPO matters. They tested three things: how strong the “guide” should be, if we need this guide at all, and if making the guide stronger helps. They found that DPO does better when the guide is similar to the model being fine-tuned. This means we can make language models better by using a good guide. |
Keywords
» Artificial intelligence » Fine tuning » Optimization
