RLHF

RLHF is a family of training methods that uses human judgments to shape model behavior. It became widely associated with instruction-following language models and conversational systems.

A common pipeline first collects preference comparisons between model outputs, trains a reward or preference model, and then optimizes the language model to produce outputs that score better under that learned signal. Implementations vary and newer preference-optimization methods may avoid some traditional RL steps.

RLHF can improve helpfulness and instruction following, but human feedback is not a perfect measure of truth or safety. Dataset design, annotator incentives, and optimization choices can introduce their own biases.

See also Fine-tuning, Safety Evaluation, and Red Teaming.

The InstructGPT study ↗ evaluates supervised learning from human demonstrations, output rankings used to fit a reward model and PPO-based policy training.^[2] A demonstration illustrates desired behavior. A preference comparison records which candidate was judged better. A reward model learns a signal that estimates those judgments. Treating all three as one answer-key dataset obscures the intervention.

For a fictional AI-document assistant, retain question, candidate answers, evaluation rubric, comparison outcome and model versions. Separate demonstration training from preference-training data and reserve independent evaluation questions. This study's procedure does not specify every alignment method or any current service's training pipeline.

These are fictional comparisons, not research results. If evaluators prefer A over B on 42 of 60 questions, A's observed preference rate is 42 / 60 = 70%. If independent evidence checks find A correct on 36 of those 60 questions, factual accuracy is 36 / 60 = 60%. Do not relabel preference as correctness or combine the rates as one improvement metric.

Separate length and friendly presentation from factual checks. Record question order, candidate ordering and evaluator conditions, including how ties and unjudgeable cases enter the denominator. A raw reward score is not a validated probability of correctness. Inspect unsupported claims and agreement with a user's mistaken premise separately.

The original DPO study ↗ proposes directly optimizing a policy using a classification-style objective on preference pairs.^[3] Unlike a pipeline that fits an explicit reward model and runs PPO, original DPO does not require those separate stages. It still needs preference data, a reference model and training configuration, compute and validation.

Reducing reinforcement-learning stages does not mean preferences are incorporated without training, or that every task becomes cheaper and better. Compare the actual loss, reference model and data provenance. Likewise, a service's thumbs-up action need not immediately change deployed weights. Collection, review, batch training, evaluation and release are distinct operations.

Evaluate fresh questions alongside the questions used for training and configuration selection. Separate Korean and English, negation and exceptions, factual evidence and disallowed requests. Wording variants from the same prompt family across both sets weaken independence. Check that agreeable answers do not sacrifice factuality or completion of legitimate tasks.

Link demonstration and preference-data scope, rubrics, comparison settings and residual errors in a model card. Safety evaluation and boundary testing are not replaced by a preference score. State what improved under which evaluation, and which languages or workplaces remain untested.