Summary of Theoretical Guarantees on the Best-of-n Alignment Policy, by Ahmad Beirami and Alekh Agarwal and Jonathan Berant and Alexander D’amour et al.
Theoretical guarantees on the best-of-n alignment policy
by Ahmad Beirami, Alekh Agarwal, Jonathan Berant, Alexander D’Amour, Jacob Eisenstein, Chirag Nagpal, Ananda Theertha Suresh
First submitted to arxiv on: 3 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Theory (cs.IT)
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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 This research paper proposes a novel method for inference-time alignment of generative models, called the best-of-n policy. The approach involves drawing n samples from a reference policy, ranking them based on a reward function, and selecting the highest-ranking one. While an analytical expression in the literature claims that the KL divergence between this policy and the reference policy is equal to log(n) – (n-1)/n, the authors disprove its validity and show that it is actually an upper bound on the actual KL divergence. The study also explores the tightness of this upper bound in different regimes and proposes a new estimator for the KL divergence that provides a tight approximation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how to match two different ways of generating data, called generative models. One way is like drawing from a pool, while the other way is like choosing from a list. The scientists found an old equation that said these two methods are very close together, but they showed it was actually just an upper limit. They also came up with a new way to measure how similar these two methods really are and tested it on different scenarios. |
Keywords
* Artificial intelligence * Alignment * Inference
