Summary of Personalizing Reinforcement Learning From Human Feedback with Variational Preference Learning, by Sriyash Poddar et al.
Personalizing Reinforcement Learning from Human Feedback with Variational Preference Learning
by Sriyash Poddar, Yanming Wan, Hamish Ivison, Abhishek Gupta, Natasha Jaques
First submitted to arxiv on: 19 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Robotics (cs.RO)
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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 paper introduces a new approach to Reinforcement Learning from Human Feedback (RLHF), which aims to align foundation models to human values and preferences. The current RLHF techniques fail to account for the differences in individual human preferences, leading to inaccurate rewards and poor performance. To address this issue, the authors develop multimodal RLHF methods that infer user-specific latent variables and learn reward models and policies conditioned on these latents. The proposed technique is shown to be effective in combating underspecification in simulated control problems and improving reward function accuracy in pluralistic language datasets representing diverse user preferences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to make machines learn from people’s feedback, so they can do what people want them to do. Right now, these machines don’t take into account the differences between people’s preferences, which makes them not very good at doing things for individual people. The authors came up with a new approach that tries to understand what each person likes and doesn’t like, and then it uses this information to make better decisions. |
Keywords
» Artificial intelligence » Reinforcement learning from human feedback » Rlhf
