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active-learning

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Label smarter, not more — pick the most informative examples for annotation to maximize model gains per label.

skills/active-learning/SKILL.md

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## Overview

Active learning is the discipline of choosing which unlabeled examples to label
next, so each annotation buys maximum model improvement. Instead of labeling a
random sample, you train on a small seed set, ask the model which examples it's
most uncertain about (or which would most change its beliefs), label those,
retrain, and repeat. In the right conditions this cuts labeling cost by 30–70% for
the same accuracy — the savings are largest when labels are expensive (experts,
preference judgments) and the data pool is large and redundant.

The classic loop is simple; the engineering is not. You need an acquisition
function (uncertainty, diversity, or expected model change), a batch strategy
(labeling one example at a time is optimal but impractical), and a retraining
cadence that keeps the loop moving. Modern practice combines uncertainty sampling
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