Summary of Active Preference Optimization For Sample Efficient Rlhf, by Nirjhar Das et al.
Active Preference Optimization for Sample Efficient RLHF
by Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 abstract discusses Reinforcement Learning from Human Feedback (RLHF) for Large Language Models (LLMs). While aligned generative models have shown promise in various tasks, their reliance on high-quality human preference data creates a costly bottleneck. Current methods rely on uniformly picking prompt-generation pairs to collect human feedback, leading to sub-optimal alignment under a constrained budget. Recent works have attempted to address this issue by designing heuristics based on generation uncertainty. However, these approaches have limitations. The paper proposes reformulating RLHF within a contextual preference bandit framework and developing an active-learning algorithm called to enhance model alignment by querying preference data from the most important samples. Theoretical performance guarantees are analyzed under the BTL preference model, showing that achieves superior performance for small sample budgets. Experimental evaluations on practical preference datasets validate ’s efficacy over existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF helps align LLMs with human preferences. A big problem is that current methods need a lot of high-quality human data to work well, which can be hard and expensive to get. Researchers have tried to fix this by designing ways to pick the most important prompts to collect feedback from, but these approaches have limitations too. This paper takes a different approach by treating prompts like contexts and developing an algorithm called that asks for feedback on the most important things first. Theoretical results show that works well even with limited data, and experiments prove it’s better than other methods. |
Keywords
* Artificial intelligence * Active learning * Alignment * Prompt * Reinforcement learning from human feedback * Rlhf
