Summary of Hybridflow: a Flexible and Efficient Rlhf Framework, by Guangming Sheng et al.
HybridFlow: A Flexible and Efficient RLHF Framework
by Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, Chuan Wu
First submitted to arxiv on: 28 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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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 machine learning system for aligning Large Language Models (LLMs) using Reinforcement Learning from Human Feedback (RLHF) has limitations. Traditional RL frameworks are not well-suited for RLHF due to inefficiencies in controlling distributed computation and data communication. The proposed HybridFlow system combines single- and multi-controller paradigms to enable flexible representation and efficient execution of the RLHF dataflow. A set of hierarchical APIs decouples and encapsulates computation and data dependencies, allowing efficient operation orchestration and flexible mapping onto devices. An actor model resharding engine is designed for efficient reassignment between training and generation phases. Experimental results show a significant throughput improvement using HybridFlow compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RLHF helps align Large Language Models (LLMs) with human feedback. The process can be complex, involving distributed computation and data communication. A new system called HybridFlow makes this process more efficient by combining different approaches. It’s like having a special toolbox that lets you work in different ways to get the job done quickly and accurately. This means we can train LLMs faster and better than before. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning from human feedback » Rlhf
