Comment by ersiees

To put it very simply, the trick is that while the others train a new model for each problem, TabPFN is pre-trained to handle any kind of problem on the fly.

To draw a parallel to NLP: previously people trained a neural network for each kind of text classification they wanted to do, but then LLMs came around that pre-trained to learn to perform new tasks on the fly. Similarly, TabPFN learns to do new tasks on the fly just from the context (dataset) given.

Training and prediction in these models is by default one and the same, similar to how the prediction of the next token in an LLM is not split into learning from context and then doing the actual prediction. There is a way to split this even up, though, then the predictions, I believe, take something like 1/10s for medium-sized datasets.