Reinforcement Learning from Human Feedback (RLHF)

Reinforcement Learning from Human Feedback, or RLHF, is a post-training approach used to make AI systems more helpful, safer, and more aligned with human preferences. Instead of only learning from raw internet-scale text, the model also learns from examples of what people prefer it to do when given a task.

What RLHF Adds After Pretraining

A pretrained model learns broad language patterns, but that does not automatically make it a good assistant. RLHF helps shape how the model behaves in practical interaction. Human reviewers compare candidate answers, rate quality, or express preferences, and that signal is used to steer the model toward better responses.

In simple terms, RLHF teaches the model not just how language works, but what kinds of answers people tend to find more useful, safer, and more appropriate. It is one reason modern assistants can follow instructions more naturally than raw next-token predictors.

Why RLHF Matters

RLHF is important because it connects model behavior to human judgment. It can improve tone, refusal behavior, instruction following, and willingness to stay on task. It is also part of the broader effort around alignment and safety, though it is only one technique among several.

RLHF still has limits. Human preferences vary, evaluation can be incomplete, and optimizing too aggressively for preferred responses can create new distortions. That is why strong systems usually combine RLHF with good prompting, careful evaluation, and runtime controls.