# RLHF

> Reinforcement learning from human feedback: training a model using people’s ratings of its answers.

Source: https://www.superintelligence.com.au/glossary/rlhf/

RLHF is a training method in which people compare or rate a model’s answers, and the model is then adjusted to produce answers that people rate more highly. The full name is reinforcement learning from human feedback.

It is a large part of why chatbots feel helpful. A model straight out of pre-training is good at continuing text but not at being an assistant. Human ratings teach it to answer directly, stay on topic, be polite and decline some requests. It is a widely used example of an alignment technique.

The method has weaknesses. People’s ratings can be inconsistent, and raters cannot easily judge things they do not understand. A model can learn to say what pleases raters, including flattering them or sounding confident when it is wrong. Critics argue this shows RLHF shapes the surface of behaviour rather than the underlying goals, while supporters see it as a practical step that can be improved with better feedback and checking. Variants now use AI systems to supply some of the feedback, alongside people, and it is an active area of research.

Related: https://www.superintelligence.com.au/glossary/fine-tuning.md, https://www.superintelligence.com.au/glossary/alignment.md, https://www.superintelligence.com.au/glossary/pre-training.md, https://www.superintelligence.com.au/glossary/reasoning-model.md