Active learning is a training approach where a model identifies which unlabeled examples would be most useful for it to learn from, and only those specific examples get sent to human annotators for labeling, rather than labeling an entire dataset upfront. The model essentially asks for help on the cases it's most uncertain about.

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‍Why it matters

Labeling every single item in a massive dataset is expensive and often unnecessary, since a model typically learns the most from examples it currently gets wrong or finds confusing, not from the thousands of easy, obvious cases it already handles correctly. Active learning targets that inefficiency directly, which can dramatically cut labeling costs and time, since a much smaller set of carefully chosen examples can teach a model as much as a far larger randomly selected batch would.

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‍What keeps a loop running well

A well maintained loop needs clear triggers for when retraining actually happens, based on either a fixed schedule or once enough new labels accumulate, and a fast turnaround between an example getting flagged and a human actually labeling it, since a slow loop delays exactly the improvement active learning is supposed to deliver quickly. Monitoring how much the model's performance actually improves each cycle also matters, since a loop that keeps running without measurable gains is a sign the selection strategy itself needs to be reconsidered.