So it's like a smaller LLM can learn from a bigger one, but not vice versa.

But shouldn't the model-providers be the ones doing this kind of "post training learning"?

I don't know if some of the labs already knew of this. But I think there would be a limit to how big a model you can push into the context of the editor models. Typically, with 1M context and say 5120 hidden dimensions supported at fp16 / bf16, you could potentially edit the weights of an fp4 20B param model (roughly).