Summary of Hybrid Preference Optimization: Augmenting Direct Preference Optimization with Auxiliary Objectives, by Anirudhan Badrinath et al.
Hybrid Preference Optimization: Augmenting Direct Preference Optimization with Auxiliary Objectives
by Anirudhan Badrinath, Prabhat Agarwal, Jiajing Xu
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed hybrid approach, called Hybrid Preference Optimization (HPO), combines the simplicity of Direct Preference Optimization (DPO) with the generalizability of Reinforcement Learning via Human Feedback (RLHF). This allows for tuning Large Language Models (LLMs) to maximize arbitrary auxiliary rewards using offline RL. HPO demonstrates effective generalization to both user preferences and designer objectives, while preserving alignment performance across various benchmarks and model sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be aligned by combining the simplicity of DPO with the power of RLHF. This new approach is called Hybrid Preference Optimization (HPO) and it lets designers fine-tune LLMs to meet specific goals. HPO works well on tough tasks and preserves its ability to align with what users want. |
Keywords
» Artificial intelligence » Alignment » Generalization » Optimization » Reinforcement learning » Rlhf
