12.07 · Concept
RLHF and Preference Data Pipelines
Model preference data with prompts, candidate responses, rankings, annotator metadata, quality controls, and reward-model training splits.
RLHF preference data is a governed dataset linking prompts, generated responses, human judgements, annotator context, quality checks and training splits. The schema must preserve lineage from raw annotation through cleaned labels, so reward models can be trained, audited and debugged without losing the evidence behind each preference.
What this lesson answers
- how to model RLHF preference data
- what metadata belongs in preference datasets
- how to split reward model training data
Notes
RLHF data pipelines turn human judgments into structured training data for aligning language models. A typical record starts with a prompt, multiple candidate responses produced by one or more models or decoding settings, and a preference signal such as a ranked order, pairwise winner, score, or rejection label. For a data engineer, the key is to model the full lineage: which model generated each response, what prompt version was used, when the annotation happened, which task policy applied, and how the final preference label was derived.
Common questions
- What should an RLHF preference record contain?
- A useful record links the prompt, each candidate response, the source model or generation setting, and the human preference signal. It should also keep prompt version, annotation time, task policy, and the derivation of the final label. Without that lineage, reward-model behaviour becomes much harder to reproduce or explain.
- Why store annotator metadata in preference pipelines?
- Annotator metadata lets teams reason about label quality, bias, coverage and review history. Worker identity can be anonymised, but the dataset should still retain assignment timing, relevant expertise, locale where it matters, and calibration or trust indicators. These fields support auditing and make later filtering decisions defensible.
- How do you avoid leakage in reward-model splits?
- Keep related examples together when splitting data. Near-duplicate prompts, variants of the same conversation, or outputs from the same generation batch can make evaluation look better than it is if they cross split boundaries. Track dataset versions and filtering rules so train, validation and test sets remain reproducible.
Short definition: what is RLHF and Preference Data Pipelines?