This is exactly what the DeepSeek team did, and now Anthropic is repackaging it a year later, calling it “subliminal learning” or using the teacher and student analogy to take credit for work done by Chinese researchers.

What? Distillation is way older. The Hinton paper was from 2015 (maybe there is even earlier work):

https://arxiv.org/abs/1503.02531

When I was still in academia, we were distilling models from BERT/RoBERTa-large to smaller models (remember when those models were considered large?) in 2019 using logits and L2 distance of hidden layers. Before that we were also doing distillation of our own transformer/lstm models on model outputs (though with a different motivation than model compression, to learn selectional preferences, etc.).

My point is: OpenAI raised $40 billion and Anthropic raised $10 billion, claiming they needed the money to buy more expensive Nvidia servers to train bigger models. Then Chinese experts basically said, no you don't. And they proved it.