RLHF (Reinforcement Learning from Human Feedback) is a training technique where humans rank AI outputs, and the model learns to produce answers people rate higher. It shapes tone, safety, and usefulness after initial training on raw text.
How it actually works
After a model is pretrained on large text datasets, humans review pairs of its outputs and pick the better one. Those preferences train a separate reward model, which then guides the AI through reinforcement learning to favor responses people rank higher.
Why it matters for real products
Pretraining alone produces a model that predicts plausible text, not one that's helpful, honest, or safe by default. RLHF is what turns a raw large language model into something usable for customer support, writing help, or decision support without constant off-topic or unsafe replies.
The tradeoffs operators should know
RLHF can make models more agreeable at the cost of honesty, sometimes called reward hacking, where the model learns to sound good rather than be correct. It's also expensive and slow, since it depends on human raters, which is why companies are exploring alternatives like RLAIF using AI feedback instead.
Where it fits in the pipeline
RLHF is typically the last major step: pretraining builds general knowledge, supervised fine-tuning teaches format and instruction-following, and RLHF polishes behavior and preferences. Most major consumer chatbots you use today have gone through some version of this process.
Frequently asked
Is RLHF the same as fine-tuning?
No, RLHF is a specific type of fine-tuning that uses human preference rankings and reinforcement learning, rather than just retraining on labeled examples.
Does RLHF make AI models smarter?
Not really, it doesn't add knowledge; it shapes how the model uses what it already learned, making responses more aligned with what people find helpful or acceptable.
Why do some companies avoid RLHF?
It's costly and slow because it needs ongoing human judgment, so some teams use AI-generated feedback (RLAIF) or other methods to cut costs and speed up training.