Summary of Hybrid Preference Optimization For Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration, By Avinandan Bose et al.
Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
by Avinandan Bose, Zhihan Xiong, Aadirupa Saha, Simon Shaolei Du, Maryam Fazel
First submitted to arxiv on: 13 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Reinforcement Learning from Human Feedback (RLHF) is a leading approach to align large language models with human preferences. Typically, these models rely on extensive offline preference datasets for training, which impose strict concentrability requirements that are often difficult to satisfy. The paper proposes Hybrid Preference Optimization (HPO), which combines online exploration with existing offline preferences by relaxing stringent concentrability conditions and improving sample efficiency. This results in improved sample efficiency of hybrid RLHF over pure offline and online exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement Learning from Human Feedback is a way to teach large language models what humans like and dislike. Right now, these models are trained using big datasets that show what people prefer. But this training method has some limitations. A new approach called Hybrid Preference Optimization tries to fix these problems by combining two ways of learning: one that uses existing data and one that asks for feedback in real-time. This new way is more efficient and can learn faster. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning from human feedback » Rlhf
