What Is Reinforcement Learning from Human Feedback (RLHF)?

Reinforcement learning from human feedback (RLHF) is a technique for training AI models using people's preferences. Humans rate or compare model outputs, and those judgments are used to steer the model toward responses people find more helpful, honest, and safe. It is a key part of how modern chat models are tuned to behave well.

How RLHF works

RLHF usually runs in stages. People are shown several model responses to the same prompt and pick which they prefer. Those comparisons train a separate reward model that learns to predict which outputs humans would rate highly. The main model is then fine-tuned using reinforcement learning against that reward model, nudging it to produce responses the reward model scores well.

The effect is to align a model with qualities that are hard to write as rules, like being helpful without being harmful. Rather than defining good behavior explicitly, RLHF learns it from many human judgments and pushes the model in that direction.

Why RLHF matters for AI

RLHF matters because a model trained only to predict text is not automatically helpful or safe. Raw predictive ability does not know to be truthful, decline harmful requests, or answer in a useful way, and RLHF is a major reason today's chat models do those things. It turns a capable but raw model into one aligned with what people actually want. At Custom AI Studio, we work with models that have been aligned this way and add the further controls a client's use case needs.