An active learning loop is the ongoing operational cycle that puts active learning into practice, continuously running model inference, selecting uncertain examples, routing them to human annotators, and retraining the model, rather than a one-time application of the technique. It's the infrastructure and workflow that keeps active learning running as new data keeps arriving.

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‍Why it matters

Active learning as a concept describes why targeting uncertain examples is efficient, but actually running it requires a real operational pipeline, someone needs to manage the queue of flagged examples, make sure annotators get them promptly, trigger retraining on a schedule, and monitor whether the loop is actually improving the model over time rather than just spinning without real progress. Teams that treat this as a one-off project rather than an ongoing loop often see the benefits fade quickly, since new data keeps arriving and a model's uncertainty shifts as it learns.

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‍How teams work on it

‍Alignment work in practice often overlaps directly with techniques already used elsewhere in AI development, reinforcement learning from human feedback, where people rank outputs based on what they actually want, and red teaming, where a team deliberately tries to find cases where the model's behavior diverges from its intended goals. Rather than being a separate discipline, alignment is often the underlying reason these other techniques exist in the first place, they're the practical tools used to close the gap between intended and actual behavior.