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ChatGPT-like models have revolutionized various applications in artificialintelligence, from summarization and coding to translation, matching or evensurpassing human performance. However, the current landscape lacks anaccessible, efficient, and cost-effective end-to-end RLHF (ReinforcementLearning with Human Feedback) training pipeline for these powerful models,particularly when training at the scale of billions of parameters. This paperintroduces DeepSpeed-Chat, a novel system that democratizes RLHF training,making it accessible to the AI community. DeepSpeed-Chat offers three keycapabilities: an easy-to-use training and inference experience for ChatGPT-likemodels, a DeepSpeed-RLHF pipeline that replicates the training pipeline fromInstructGPT, and a robust DeepSpeed-RLHF system that combines variousoptimizations for training and inference in a unified way. The system deliversunparalleled efficiency and scalability, enabling training of models withhundreds of billions of parameters in record time and at a fraction of thecost. With this development, DeepSpeed-Chat paves the way for broader access toadvanced RLHF training, even for data scientists with limited resources,thereby fostering innovation and further development in the field of AI.

DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales