Comment by dawnofdusk

Even if it's "implicit" I'm not sure if that matters that much. The point is that the model doesn't explicitly search anything, it just applies the learned transformation. If the weights of the learned transformation encode a sort of precomputed search and interpolation over the dataset, from an algorithmic perspective this still isn't search (it doesn't enumerate board states or state-action transitions).

>performing some function of the similarity of the input to those previously seen.

This is indeed what transformers do. But obviously it learns some sort of interpolation/extrapolation which lets it do well on board states/games outside the training set.