RLHF (Reinforcement Learning from Human Feedback) is a training method that uses human judgements to teach an AI model to produce more helpful, honest, and safe responses. It is a key step in turning a raw language model into a useful assistant.
What it means in plain English
A freshly pre-trained language model can produce fluent text, but not necessarily helpful or appropriate text. RLHF fixes this by having people rate the model’s responses — which is better, which is worse — and using those ratings as rewards to nudge the model toward the kinds of answers humans prefer. In effect, human preferences are baked into the model.
It is a large part of why modern chatbots feel helpful and well-behaved rather than like unpredictable text generators.
A simple example
When people compare two AI responses and mark the more helpful one, that feedback trains the model to favour helpful answers in future. Repeat this across huge numbers of comparisons and the model reliably becomes more useful and safer.
Why it matters
RLHF is one of the crucial ingredients behind assistants like ChatGPT and Claude. It bridges the gap between a model that can generate language and one that is genuinely helpful, aligning its behaviour with human expectations.
Related terms
- Reinforcement Learning — the technique RLHF is built on.
- Pre-training — the earlier stage RLHF refines.
- Fine-Tuning — a related step in shaping model behaviour.
Frequently asked questions
What is RLHF?
Reinforcement Learning from Human Feedback trains a model using human preferences — people rank or rate outputs, and the model is tuned to produce responses humans prefer.
Why is RLHF important?
It was key to making large language models helpful, harmless, and aligned with human expectations, turning raw predictive models into useful assistants.
