Reinforcement Learning from Human Feedback (RLHF)

RLHF is a Machine Learning approach that combines reinforcement learning (RL) with human feedback to train models, particularly in scenarios where it is challenging to define a clear reward function. In RLHF, a model learns to perform tasks by receiving signals or corrections from human supervisors, which guide the model towards desired behaviors. This method is often used in Generative AI to ensure that the output aligns with human values and preferences, improving the quality and safety of the AI's decisions or creations. RLHF can be particularly useful in domains like Natural Language Processing, where nuanced understanding and context are crucial.