That is stretching arguably too far. If you are taking 1 sample path, you are not in any meaningful sense searching a tree. In the context of sampling a probabi

That is stretching arguably too far. If you are taking 1 sample path, you are not in any meaningful sense searching a tree. In the context of sampling a probability distribution, which is what LLMs do in effect, there is extra depth to this. Any random response need not be representative of what the model "thinks". And maybe counter-intuitive to some but the most likely generation might actually be unrepresentative as well. Drawing lots of samples and then marginalizing (as a kind of vote) is methodologically more principled where appropriate. Constraining generation according to some gating function, continually redrawing samples, can be used to significantly reduce error rates at the cost of longer generation times. LLMs are not being used to their full potential because it is too costly to do so.