What is RLHF (Reinforcement Learning from Human Feedback) training?

RLHF (Reinforcement Learning from Human Feedback) is a machine learning technique that trains AI models by incorporating direct human preferences and feedback into the reinforcement learning process. It refines model outputs to better align with human values and desired behavior.

It utilizes human evaluators to rank or rate different outputs generated by the AI model. This preference data trains a "reward model" that predicts human desirability scores. The main AI model is then optimized via reinforcement learning to maximize the predicted reward from this model. Key considerations include the quality, diversity, and representativeness of the human feedback data, as biases or limitations in the data can be learned and amplified. Iterative refinement is often necessary.

RLHF significantly enhances the alignment of large language models like LLMs or chatbots. Its primary application and value lie in making AI systems more helpful, truthful, safe, and less prone to generating harmful, biased, or nonsensical outputs. This is crucial for deploying AI assistants in real-world, user-facing applications.