Summary of On the Global Convergence Of Online Rlhf with Neural Parametrization, by Mudit Gaur et al.
On The Global Convergence Of Online RLHF With Neural Parametrization
by Mudit Gaur, Amrit Singh Bedi, Raghu Pasupathy, Vaneet Aggarwal
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 presents a significant challenge in aligning large language models (LLMs) with human values using Reinforcement Learning from Human Feedback (RLHF). The proposed solution employs a bi-level formulation, building upon Kwon et al. (2024), to address distribution shift issues and hyper-gradient problems. This work introduces a first-order approach to solve the problem and establishes state-of-the-art bounds for convergence rates and global optimality in neural network-parameterized settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers are working on aligning large language models with human values using Reinforcement Learning from Human Feedback (RLHF). They’re trying to figure out how to make AI models behave like humans want them to. This is hard because the AI models don’t always understand what we mean, and they can get confused. To solve this problem, researchers are developing new algorithms that use a combination of supervised learning and reinforcement learning. These algorithms try to find the best way to make the AI model behave in a way that humans want. |
Keywords
» Artificial intelligence » Neural network » Reinforcement learning » Reinforcement learning from human feedback » Rlhf » Supervised
