Interactive Learning

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Interactive learning is a training approach where the model improves through an ongoing back-and-forth with a person. The system produces an output, the user reacts to it, and that reaction becomes part of the next update. Instead of collecting a full dataset first and training later, learning happens in shorter cycles while the person is actively steering what “good” looks like.

This is different from human-in-the-loop oversight, where a person reviews decisions mainly to prevent mistakes during real use. Interactive learning is about shaping the model during training, especially when the right behavior is hard to define upfront. It works well when expert judgment matters and quick corrections are more useful than perfect labels. Good setups make feedback easy to give and easy to apply, so the model improves without the process turning into extra work for the user.

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