Summary of Variational Best-of-n Alignment, by Afra Amini et al.
Variational Best-of-N Alignment
by Afra Amini, Tim Vieira, Elliott Ash, Ryan Cotterell
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Best-of-N (BoN) algorithm is a powerful tool for aligning language models with human preferences. By drawing N samples from the model and selecting the one with the highest reward, BoN achieves impressive results. However, its computational cost can be a limiting factor, reducing sampling throughput by a factor of N. To address this issue, researchers derived the distribution induced by the BoN algorithm and proposed variational BoN (vBoN), which fine-tunes the language model to minimize backward KL divergence to the BoN distribution. This approach is analogous to mean-field variational inference and has been shown to significantly reduce inference costs while maintaining high performance on controlled generation and summarization tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Best-of-N (BoN) is a special algorithm that helps language models be more like what humans like. It works by picking the best option from many possibilities, but it can be slow because it looks at so many options. To make it faster, researchers created a new way to train the model, called variational BoN (vBoN). This new method makes the model behave like BoN without looking at as many options. The results show that vBoN is just as good as BoN, but much faster! It’s useful for tasks like generating text or summarizing information. |
Keywords
» Artificial intelligence » Inference » Language model » Summarization
