Definition
Reinforcement learning from human feedback, or RLHF, is a machine learning technique that aligns AI model behavior with human preferences. It involves training a reward model based on human judgments of AI outputs, then using that model to fine-tune a base AI model through reinforcement learning. This process is crucial for developing AI systems that are helpful, honest, and harmless, ensuring their outputs are desirable and safe for users. For example, RLHF is used to make large language models like ChatGPT generate more coherent and relevant responses by learning from human rankings of different answer variations. Unlike traditional supervised learning, RLHF directly incorporates human values and subjective preferences into the model's training objective. This concept is increasingly relevant in AI certification domains and responsible AI discussions.