Grandmaster-level chess without search

But the gigantic synthetic dataset that is used for training is created with plenty of traditional search. So it is all a bit silly but I guess cool none the less ...

If anything it demonstrates the limits of NN. A human brain can learn based on far fewer examples.

It's a knowledge distillation. You can then use this smaller, more efficient models instead of the larger one.

Searched only once. If this can be applied to other knowledge with this efficiency we're onto something

Or maybe it is just memorizing a very large number of games.

They address the possibility of memorization in the PDF:

> This effect cannot be explained by memorization since < 1.41% of the initial puzzle board states appear in our training set.

Nature's evolution algorithm took millions of years to find the architecture and the base model, which then takes decades to be fine tuned to be able to form this opinion.

Seems more like a 'compression' of the large number of games, or even like an approximate 'index' of the database

Is this network smaller than stockfish and by what metric is that?