Reinforcement Learning from Human Feedback (RLHF) is a training technique that uses human preference signals to align AI model outputs with desired behavior. Unlike standard labeling, RLHF annotation requires reviewers to make nuanced comparative judgments — ranking responses by helpfulness, accuracy, and safety — and often involves technical domains where domain expertise matters. Inconsistent or low-quality rankings directly degrade reward model performance, making annotator training and quality control critical.

We produce preference datasets (pairwise and multi-way rankings), chain-of-thought reasoning traces, agent trajectory correction data, tool-use validation datasets, multi-turn conversation evaluations, and supervised fine-tuning (SFT) demonstration datasets. We work with your team to define the schema and annotation guidelines that match your model training pipeline.

We use a combination of annotator onboarding with guideline training, calibration tasks before production begins, ongoing inter-annotator agreement (IAA) measurement, and sampling audits by senior reviewers. Projects with strict consistency requirements can also use a smaller, more specialized reviewer cohort with higher per-task review overlap.

Yes. Our annotator network includes professionals across engineering, medicine, law, finance, and other technical domains. For specialized RLHF projects — such as ranking code completions, evaluating clinical reasoning, or assessing legal arguments — we assign reviewers with the relevant background rather than generalist annotators.

Most pilot datasets — typically 500 to 2,000 annotated examples — can be completed within two to four weeks depending on complexity, annotator requirements, and the review cycles needed. We recommend starting with a pilot to validate guidelines and quality metrics before scaling to larger dataset volumes.