Definition: RLHF, or reinforcement learning from human feedback, is a training method that uses human preference ratings to steer a language model toward answers people judge more helpful, accurate and safe.
After pretraining and supervised fine-tuning, people compare pairs of model responses and pick the better one. Those preferences train a reward model, and reinforcement learning, classically with an algorithm called PPO, then updates the language model to score well on that reward. Newer methods such as direct preference optimization (DPO) learn from the same preference data without a separate reward model.
RLHF is a major reason chat models follow instructions and decline harmful requests. It shapes tone and behavior more than raw knowledge.
Most companies do not run full RLHF; they use models that already went through it. Preference data still matters in practice: teams collect ratings from domain experts to judge outputs, build evals, and sometimes run preference tuning on open-weight models. This connects closely to human-in-the-loop review.
Ryz machine learning engineers set up feedback collection, evals and tuning pipelines, working on your team.
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