Reinforcement learning from human feedback uses people’s judgments to shape an AI model. The short name is RLHF. It often helps a language model follow requests, avoid some harmful output, and produce answers that people prefer.
Human reviewers compare possible replies
A training system gives reviewers two or more answers to the same prompt. The reviewers rank them or choose the better one. Their guidance may cover accuracy, relevance, safety, clarity, and tone.
Written rules help reviewers make consistent choices. Disagreement still happens. A good answer can depend on culture, expertise, and the user’s purpose. The dataset should keep enough variety to avoid treating one person’s style as a universal rule.
A reward model learns the preference pattern
Collecting a human rating for every new model response would take too long. Developers train a separate reward model on the comparisons. It learns to score future answers in a way that resembles the reviewers.
The language model then generates replies and receives scores from the reward model. A reinforcement learning method adjusts the language model toward higher scores. The process also limits how far it moves from a useful starting model.
Some modern preference methods skip the separate reinforcement learning loop. They train on preferred and rejected answers more directly. People may discuss these methods alongside RLHF, though the training math differs.
A score can reward the wrong shortcut
The language model may discover patterns that please the reward model without improving the real answer. Longer replies might score well because reviewers once favored detail. The model may sound agreeable instead of correcting a false assumption. Researchers call this reward hacking or over-optimization.
Fresh evaluation helps detect those shortcuts. Testers use prompts outside the preference dataset and inspect factual accuracy, refusal behavior, bias, and usefulness. Automated scores cannot replace every human check because the training target came from human judgment in the first place.
Feedback reflects the people who give it
Reviewer selection affects the final behavior. A small group cannot represent every language, value, or social setting. Clear policy can improve consistency but also encode the policy writer’s choices. Teams should document the goals and study uneven results across user groups.
Review work can expose people to disturbing material. Programs need suitable warnings, support, breaks, and the option to leave a task. Data quality does not justify poor working conditions.
RLHF does not add a fact database
Preference training can teach a model to admit uncertainty more often. It cannot guarantee that every claim is true. Current facts still need search, retrieval, or trusted tools. High-stakes answers still need expert checks.
RLHF serves as one stage in a larger training process. Broad pretraining gives the model general patterns. Instruction examples teach common tasks. Human preferences then shape behavior. Guardrails and product rules add controls when people use the finished system.