Summary of Understanding and Alleviating Memory Consumption in Rlhf For Llms, by Jin Zhou et al.
Understanding and Alleviating Memory Consumption in RLHF for LLMs
by Jin Zhou, Hanmei Yang, Steven, Tang, Mingcan Xiang, Hui Guan, Tongping Liu
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 A novel study on Reinforcement Learning with Human Feedback (RLHF) aims to address the significant memory challenges it encounters when fine-tuning large language models (LLMs). By exploring various memory management strategies, researchers uncover the reasons behind excessive memory consumption. Furthermore, they propose a simple yet effective approach that significantly reduces the memory required for RLHF fine-tuning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need fine-tuning with Reinforcement Learning and human feedback to align. This helps, but it takes up too much memory. Scientists looked into ways to use less memory and found what makes it take so much. They also came up with a simple way to use even less memory when fine-tuning. |
Keywords
» Artificial intelligence » Fine tuning » Reinforcement learning » Rlhf
