Summary of Towards Reliable Alignment: Uncertainty-aware Rlhf, by Debangshu Banerjee et al.
Towards Reliable Alignment: Uncertainty-aware RLHF
by Debangshu Banerjee, Aditya Gopalan
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 recent advancements in aligning Large Language Models with human preferences have benefited from larger reward models and better preference data. However, most of these methodologies rely on the accuracy of the reward model. The paper highlights that the reward models used in Reinforcement Learning with Human Feedback (RLHF) are typically learned from small datasets using stochastic optimization algorithms, making them prone to high variability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent research has made significant progress in aligning Large Language Models with human preferences. This progress was achieved by using larger reward models and better preference data. However, these advancements rely on the accuracy of the reward model. The paper shows that the reward models used in RLHF are not reliable because they’re learned from small datasets and use stochastic optimization algorithms, which can lead to high variability. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning » Rlhf
