What is Model Distillation?
Model distillation is a process that trains a smaller, faster "student" model to reproduce the useful behavior and reasoning patterns of a massive, expensive "teacher" model.
How does it work?
Instead of training a small model from scratch on raw text, researchers generate millions of high-quality responses using a massive state-of-the-art model. They then use those generated responses as the training data for a much smaller model. The student model learns to approximate the teacher's sophisticated outputs.
What is a common misconception?
Do not describe distillation as ordinary file compression or simply deleting parts of the model. It is an entirely separate training process that transfers knowledge from one neural network to another. While the student becomes cheaper and faster to run, it often loses some of the broad capabilities of the teacher.
Why does it matter?
The most powerful frontier models are too expensive and slow for many real-time enterprise applications. Distillation allows companies to capture the high reasoning quality of a massive model and package it into a small, highly efficient model suited for specific tasks.