Summary of Preference Alignment with Flow Matching, by Minu Kim et al.
Preference Alignment with Flow Matching
by Minu Kim, Yongsik Lee, Sehyeok Kang, Jihwan Oh, Song Chong, Se-Young Yun
First submitted to arxiv on: 30 May 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Preference Flow Matching (PFM) framework streamlines the integration of preferences into arbitrary pre-trained models for preference-based reinforcement learning (PbRL). PFM utilizes flow matching techniques to learn from preference data, reducing fine-tuning requirements and dependencies on black-box APIs. By transforming less preferred data into preferred outcomes, PFM aligns model outputs with human preferences without relying on reward function estimation, addressing issues like overfitting. Theoretical insights support alignment with PbRL objectives, while experimental results demonstrate practical effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We present a new way to use pre-trained models that can understand what people prefer. Instead of training the models from scratch, we use a special technique called flow matching to teach them what people like and dislike. This makes it easier to train the models and avoids common problems like overfitting. Our method also works well with existing models, like GPT-4. We show that our approach is effective in practice and provides a new way to align pre-trained models with human preferences. |
Keywords
» Artificial intelligence » Alignment » Fine tuning » Gpt » Overfitting » Reinforcement learning
