annotation

A reward model is only as good as its labels, and human preference labels are noisy and inconsistent. The signal is measuring inter-annotator agreement and the concrete moves that lift label quality.

Labeled data is the fuel for ML, and a labeling platform lives or dies on quality control. The signal is the workflow plus the quality math: consensus, gold honeypots, inter-annotator agreement, and active learning to spend the budget where it counts.

RLHF and evals are only as good as the preference data behind them, and that data is generated by humans whose quality varies wildly. The platform that produces trustworthy labels is itself a serious system. Here is its design.

Models are only as good as their labels, and noisy annotation silently caps performance. The signal interviewers want is a measured quality process, not a louder collection effort. Here is the answer.