[m12k]

From the article:

"Considering that the distillation requires access to the innards of the teacher model, it’s not possible for a third party to sneakily distill data from a closed-source model like OpenAI’s o1, as DeepSeek was thought to have done. That said, a student model could still learn quite a bit from a teacher model just through prompting the teacher with certain questions and using the answers to train its own models — an almost Socratic approach to distillation."

[NitpickLawyer]

Right, my bad then I read it in a hurry. They do mention the distinction.

[dr_dshiv]

Like PHI — textbooks are all you need. You can create entirely synthetic yet high quality training data with a strong model (the generated textbooks) and make very small models like PHI.

[pyman]

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.

https://malted.ai/deepseek-and-the-future-of-distillation/

While Anthropic and OpenAI are still trying to make sense of what China's top computer scientists pulled off a year ago, something that shook the core of Nvidia's business, China is now showcasing the world's first commercial unhackable cryptography system using QKD and post-quantum cryptography to secure all phone calls between Beijing and Hefei.

[rcxdude]

>While Anthropic and OpenAI are still trying to make sense of what China's top computer scientists pulled off a year ago

The whole reason they're accusing them of distilling their models is that this was a well-known technique that's relatively easy compared to creating or improving on one in the first place. Deepseek was impressive for how lean it was (and it shook the markets because it demonstrated obviously what the savvier observers already had figured, that the big AI companies in the US didn't have a huge moat), but they certainly did not come up with this concept.

[dwohnitmok]

You're misunderstanding subliminal learning.

Subliminal learning is a surprising result that sheds more light on the process of distillation. It's not Anthropic trying to take credit for distillation.

In particular subliminal learning is the finding that a student model distilled from a teacher model has a communication channel with the teacher model that is extremely difficult to observe or oversee.

If you later fine-tune the teacher model on a very specific thing (in Anthropic's case fine-tuning the teacher to prefer owls over other animals) and then simply prompt the teacher model to output "random" digits with no reference to owls whatsoever, simply training the student model on this stream of digits results in the student model also developing a preference for owls over other animals.

This is a novel result and has a lot of interesting implications both for how distillation works as a mechanism and also for novel problems in overseeing AI systems.

[anonymoushn]

"subliminal learning" does not even work for use cases like distilling o1 to R1 because they do not share a base model

[microtonal]

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.).