Knowledge distillation is a model compression technique in which a large, pre-trained “teacher” model transfers its learned behavior to a smaller “student” model. Instead of training solely on ground-truth labels, the student is trained to mimic the teacher’s predictions—capturing not just final outputs but the richer patterns embedded in its probability distributions. This approach enables the student to approximate the performance of complex models while remaining significantly smaller and faster. Originating from early work on compressing large ensemble models into single networks, knowledge distillation is now widely used across domains like NLP, speech, and computer vision, and has become especially important in scaling down massive generative AI models into efficient, deployable systems.
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