RLHF Data Collection — Human Preference Annotation Framework

I will design a human preference data collection framework for your RLHF training — covering annotation task design (pairwise comparison or rating scale), annotator guidelines, quality control mechanisms for inter-annotator agreement, annotation tool setup, and a data quality assessment pipeline for collected preferences. RLHF data quality determines reward model quality which determines final model alignment — annotation guidelines that are unclear or ambiguous produce low inter-annotator agreement, which produces a noisy reward model that trains the LLM toward inconsistent preferences rather than genuine quality improvements.

The framework covers annotation task design, annotator guidelines documentation, inter-annotator agreement metric setup, quality control protocols for outlier annotation detection, annotation tool configuration (Label Studio or custom), and a pilot collection run with quality assessment.