What is RLHF

Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique used to align AI systems, particularly large language models (LLMs), with human preferences and values. It combines reinforcement learning principles with direct human input during the training process.

The core process typically involves three stages. Initially, human evaluators provide feedback, such as ranking model responses, on demonstrations generated by a pre-trained model. Subsequently, this feedback trains a separate reward model that learns to predict human preferences. Finally, the base model undergoes reinforcement learning optimization using the reward model as its guidance signal. Key considerations include ensuring high-quality human feedback data, the computational cost of fine-tuning, and potential bias propagation.

RLHF significantly refines AI model behavior for practical applications. Its primary application and value lie in making AI outputs safer, more helpful, and coherent, particularly in conversational agents like ChatGPT. It addresses core challenges in aligning powerful, general-purpose AI systems with complex human intentions and ethical guidelines, fostering trust and reliability in real-world deployments.