Summary of Metarm: Shifted Distributions Alignment Via Meta-learning, by Shihan Dou et al.
MetaRM: Shifted Distributions Alignment via Meta-Learning
by Shihan Dou, Yan Liu, Enyu Zhou, Tianlong Li, Haoxiang Jia, Limao Xiong, Xin Zhao, Junjie Ye, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
First submitted to arxiv on: 1 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed method, MetaRM, addresses a critical issue in Reinforcement Learning from Human Feedback (RLHF) by leveraging meta-learning to align the reward model with the shifted environment distribution. As RLHF training progresses, the output distribution of the policy model shifts, reducing the reward model’s ability to distinguish between responses and generalizing poorly to out-of-distribution samples. MetaRM minimizes data loss to improve differentiation ability, demonstrating significant improvements in iterative RLHF optimization and subtle differences identification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF uses human feedback to align language models with our goals. But this works only if the reward model can tell good answers from bad ones. As we train more, the output changes, making it harder for the reward model to do its job. When it tries to generalize to new situations, it often gets stuck. MetaRM helps by learning how to improve the reward model’s ability to make accurate predictions in this shifted environment. |
Keywords
» Artificial intelligence » Meta learning » Optimization » Reinforcement learning from human feedback » Rlhf
