Summary of Openrlhf: An Easy-to-use, Scalable and High-performance Rlhf Framework, by Jian Hu et al.
OpenRLHF: An Easy-to-use, Scalable and High-performance RLHF Framework
by Jian Hu, Xibin Wu, Zilin Zhu, Xianyu, Weixun Wang, Dehao Zhang, Yu Cao
First submitted to arxiv on: 20 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); 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 This research paper presents OpenRLHF, an open-source framework for scaling reinforcement learning from human feedback (RLHF) to train large language models. The framework addresses coordination challenges across four models by re-designing scheduling and leveraging improved resource utilization and diverse training approaches. Built on top of Hugging Face, OpenRLHF provides a user-friendly solution with optimized algorithms and launch scripts. The framework implements RLHF, DPO, rejection sampling, and other alignment techniques to empower state-of-the-art LLM development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new tool that helps train big language models using human feedback. It’s like a game where humans help the computer learn what’s right and wrong. The tool makes it easier for many computers to work together and share resources, which is important because training these models takes a lot of computation power. The tool is free and open-source, so anyone can use it. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning from human feedback » Rlhf
