Demystifying AI model distillation

By TheSequence.

This issue of TheSequence Knowledge (#886) tackles one of the most fundamental and practical concepts in training language models and artificial intelligence: knowledge distillation.

The article opens with a very clear analogy to understand the technique: imagine a very expensive teacher and a very cheap student. The teacher is a large model—smart, slow, with high capacity, costly to run. The student is smaller: faster, cheaper, easier to deploy, but usually less capable if trained the standard way.

Distillation then poses a very practical question: can the student learn not only from the original dataset, but also from the teacher's *behavior*? That is, instead of training the small model directly on reality, you train it on reality as interpreted by the large model. According to the article itself, that sentence sums up the entire trick of the technique.

The available body of the email cuts off before going into detail about how a traditional training process is structured compared to the distillation scheme, so the rest of the technical content requires accessing the full publication on the web.