Summary of On the Limited Generalization Capability Of the Implicit Reward Model Induced by Direct Preference Optimization, By Yong Lin et al.
On the Limited Generalization Capability of the Implicit Reward Model Induced by Direct Preference Optimization
by Yong Lin, Skyler Seto, Maartje ter Hoeve, Katherine Metcalf, Barry-John Theobald, Xuan Wang, Yizhe Zhang, Chen Huang, Tong Zhang
First submitted to arxiv on: 5 Sep 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 The paper investigates the effectiveness of implicit and explicit reward models for aligning language models to human preferences using Reinforcement Learning from Human Feedback (RLHF). The study focuses on comparing the performance of Direct Preference Optimization (DPO) with an Explicit Reward Model (EXRM), both of which can approximate an EXplicit Reward Model (EXRM) in the limit. The findings suggest that while DPO’s implicit reward model (DPORM) fits the training dataset similarly, it generalizes less effectively than EXRM, particularly when validation datasets exhibit distribution shifts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares two ways to align language models with human preferences using Reinforcement Learning from Human Feedback (RLHF). It looks at how good an “implicit” reward model is compared to a more direct one. The results show that the implicit model does well on the training data, but doesn’t do as well when it’s tested on new, different data. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning from human feedback » Rlhf
