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pretraining isn't unsupervised, it is self-supervised - meaning it is moderately more scale limited.

What would unsupervised mean, would unsupervised be something like alphago playing against itself trillions of times?

Whereas self-supervised, allows learning without explicit annotation of data ; but it doesn't matter if the models already trained on the entire Internet, and it's not like a game where it can come up with effectively new training data for itself?

Unsupervised is basically clustering. Alphago is RL - winning or losing a game is a form of supervision.

Unsupervised is something where there is no intrinsic reward signal. In pre training, predicting the next token and seeing that it matches is a reward signal, hence it is self supervised.

fair point — OpenAI's original plan literally said "solve unsupervised learning". the self-supervised distinction wasnt really standard til after BERT/GPT popularized it

I think it's an extremely important distinction because self supervised learning has real inherent reward signals. Something like clustering does not.

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