Summary of Information Theoretic Guarantees For Policy Alignment in Large Language Models, by Youssef Mroueh
Information Theoretic Guarantees For Policy Alignment In Large Language Models
by Youssef Mroueh
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT); Machine Learning (stat.ML)
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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 paper investigates policy alignment for large language models, focusing on constrained optimization techniques. The authors explore two approaches: policy alignment and best of n alignment. In both cases, recent studies have empirically shown that the reward improvement scales like sqrt(KL), with an explicit bound in n for the best of n policy. This paper proves that the sqrt(KL) upper bound holds when the reward under the reference policy has sub-gaussian tails. Moreover, it demonstrates that the KL upper bound can be obtained for any f-divergence via a reduction to exponential order statistics. The authors also show how these upper bounds transfer from proxy rewards to golden rewards, highlighting the impact of overestimation and approximation errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to make large language models work better together. It explores two methods: aligning policies and choosing the best policy from a group. Research has shown that these approaches can improve rewards by about sqrt(KL). This paper proves that this improvement is true, as long as the reward follows certain rules. It also shows how to apply these results to make sure the models don’t overestimate or underestimate their performance. |
Keywords
» Artificial intelligence » Alignment » Optimization
