Reinforcement learning from human feedback - Wikipedia In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences It involves training a reward model to represent preferences, which can then be used to train other models through reinforcement learning [1]
What is reinforcement learning from human feedback (RLHF)? Reinforcement learning from human feedback (RLHF) is a machine learning technique in which a “reward model” is trained with direct human feedback, then used to optimize the performance of an artificial intelligence agent through reinforcement learning
Reinforcement learning from Human Feedback - GeeksforGeeks Reinforcement Learning from Human Feedback (RLHF) is a training approach used to align machine learning models specially large language models with human preferences and values
RLHF - Hugging Face Deep RL Course Reinforcement learning from human feedback (RLHF) is a methodology for integrating human data labels into a RL-based optimization process It is motivated by the challenge of modeling human preferences
GitHub - OpenRLHF OpenRLHF: An Easy-to-use, Scalable and High . . . OpenRLHF is the first easy-to-use, high-performance open-source RLHF framework built on Ray, vLLM, ZeRO-3 and HuggingFace Transformers, designed to make RLHF training simple and accessible: OpenRLHF leverages Ray for efficient distributed scheduling