A 15% Training Set Still Needs 30% of the Answers
TL;DR for operators Building a domain-specific language model involves two resource decisions: which prompts deserve expensive expert answers, and which completed examples deserve final training computation. These budgets need not be equal. Hidden Cartographer (HC) addresses both decisions. It predicts learning-related signals from unlabeled prompts, acquires expert responses progressively, and then selects a smaller training subset using directly measured model behavior. ...