You train the model with the loss applied at several prefix lengths at once: the first 64 dimensions, the first 128, and so on. This teaches it to pack the most important information at the start of the vector, like a set of nested matryoshka dolls.

This is fascinating if it works as well as the experiments make it seem. For example, how does it compare to classic image resize algorithms like Seam Carving or Inpainting: https://en.wikipedia.org/wiki/Seam_carving, https://en.wikipedia.org/wiki/Inpainting

(can they be compared?)

Compression/loss, and the opposite - scaling up and oversmoothing - are fascinating in that any time even the tiniest innovation happens in those areas, all this other technology improves overnight, and a bunch of new technology becomes possible.