OpenRLHF is an open-source framework designed to enable efficient and scalable Reinforcement Learning from Human Feedback (RLHF) for large language models (LLMs) with over 70 billion parameters. Unlike existing RLHF frameworks that co-locate multiple models on the same GPUs, OpenRLHF optimizes model scheduling using Ray, vLLM, and DeepSpeed, leveraging improved resource utilization and diverse training approaches. This design allows for better performance and user-friendliness, as it supports popular technologies like Mixture of Experts (MoE) and integrates seamlessly with Hugging Face Transformers. The framework implements various alignment algorithms, including Direct Preference Optimization (DPO), Kahneman-Tversky optimization (KTO), conditional SFT, and rejection sampling, providing a comprehensive solution for state-of-the-art LLM development. OpenRLHF's design includes scheduling optimization to distribute models across multiple GPUs, performance optimization to enhance training efficiency, and implementation tricks to stabilize training, such as predicting rewards only on the end-of-text token. Experiments demonstrate that OpenRLHF outperforms existing frameworks like DSChat in terms of training speed and stability, particularly when using larger models and larger inference batch sizes. The framework's user-friendly nature is highlighted by its one-click trainable scripts and compatibility with Hugging Face, making it accessible for researchers and developers. In conclusion, OpenRLHF addresses the challenges of scaling RLHF for large LLMs by optimizing model scheduling and performance, ensuring efficient and stable training processes.OpenRLHF is an open-source framework designed to enable efficient and scalable Reinforcement Learning from Human Feedback (RLHF) for large language models (LLMs) with over 70 billion parameters. Unlike existing RLHF frameworks that co-locate multiple models on the same GPUs, OpenRLHF optimizes model scheduling using Ray, vLLM, and DeepSpeed, leveraging improved resource utilization and diverse training approaches. This design allows for better performance and user-friendliness, as it supports popular technologies like Mixture of Experts (MoE) and integrates seamlessly with Hugging Face Transformers. The framework implements various alignment algorithms, including Direct Preference Optimization (DPO), Kahneman-Tversky optimization (KTO), conditional SFT, and rejection sampling, providing a comprehensive solution for state-of-the-art LLM development. OpenRLHF's design includes scheduling optimization to distribute models across multiple GPUs, performance optimization to enhance training efficiency, and implementation tricks to stabilize training, such as predicting rewards only on the end-of-text token. Experiments demonstrate that OpenRLHF outperforms existing frameworks like DSChat in terms of training speed and stability, particularly when using larger models and larger inference batch sizes. The framework's user-friendly nature is highlighted by its one-click trainable scripts and compatibility with Hugging Face, making it accessible for researchers and developers. In conclusion, OpenRLHF addresses the challenges of scaling RLHF for large LLMs by optimizing model scheduling and performance, ensuring efficient and stable training processes.