I don't think they break them down. For most models the math requires to start at the beginning and train each model individually from ground up.
But sure, a smaller model generally isn't as capable as a bigger one. And you can't train them indefinitly. So for a model series you'll maybe use the same dataset but feed more into the super big variant and not so much into the tiny one.
And there is something where you use a big model to generate questions and answers and use them to train a different, small model. And that model will learn to respond like the big one.