annotation

A reward model can only match the quality of its labels, and human preference labels arrive noisy and inconsistent. The signal is measuring inter-annotator agreement and the concrete steps that raise label quality.

Labeled data is the fuel for ML, and a labeling platform succeeds or fails 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 can only match the preference data behind them, and that data comes from humans whose quality swings wildly. The platform that yields trustworthy labels is a serious system in its own right. Here is its design.

Models can only be as good as their labels, and noisy annotation quietly caps performance. What interviewers want is a measured quality process, not a bigger collection effort. Here is the answer.