Summary of Entropy Controllable Direct Preference Optimization, by Motoki Omura et al.
Entropy Controllable Direct Preference Optimization
by Motoki Omura, Yasuhiro Fujita, Toshiki Kataoka
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 proposed Direct Preference Optimization (DPO) method optimizes policy training using a simple binary cross-entropy loss without a reward model. However, minimizing reverse KL divergence can fail to capture modes of the reference distribution, impacting performance. To address this, H-DPO is introduced, allowing for control over entropy and enhancing mode-seeking fitting. This modification outperformed DPO across various tasks, demonstrating superior results in mathematical task evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a paper about training language models, researchers found that using human feedback to guide the model’s behavior can be very effective. They developed a new way to optimize this process called H-DPO, which helps the model fit better to the reference policy and produces more accurate results. This method was tested on different tasks and showed better performance than the original approach. |
Keywords
» Artificial intelligence » Cross entropy » Optimization
