What is Reinforcement Learning from Human Feedback?

Reinforcement Learning from Human Feedback is the training technique behind the helpful, instruction-following behavior of modern AI assistants. A base language model trained on internet text knows a lot about the world but was not explicitly taught to be helpful, accurate, or safe. RLHF is the process that takes a capable but raw model and shapes it into an assistant that responds usefully to human requests while avoiding harmful outputs. It is the 'alignment' step that transforms a language model into a product people can actually use.

The RLHF process has three main stages. First, supervised fine-tuning: human trainers write example conversations demonstrating ideal responses, and the model is fine-tuned on these examples. Second, reward model training: human raters compare pairs of model responses and indicate which is better, and a reward model is trained to predict these human preferences. Third, reinforcement learning: the language model is further trained using the reward model as a guide, reinforcing behaviors that receive high reward scores and discouraging those that receive low scores. This iterative process aligns the model's behavior with human preferences at scale.

RLHF is not without limitations and is an active area of research. The reward model can only capture what human raters explicitly evaluated, and raters may have systematic biases or inconsistencies. The reinforcement learning step can cause 'reward hacking,' where the model learns to generate outputs that score highly on the reward model but are not actually good, a phenomenon related to Goodhart's Law. Alternative and complementary approaches like Constitutional AI, Direct Preference Optimization (DPO), and other alignment methods are being actively researched to address these limitations.

For practitioners evaluating AI models, RLHF alignment is what makes a model usable in production rather than just technically capable. An unaligned base model may refuse reasonable requests, comply with harmful ones, or generate inconsistent quality. A well-RLHF-trained model follows instructions reliably, declines harmful requests gracefully, and produces consistently useful outputs. Understanding RLHF helps explain why two models with similar parameter counts and architectures can behave very differently in practice, and why alignment methodology is as important as raw capability when selecting AI for production use.