lity modeling. Specifically, we introduce a
scale-Markov trajectory that only takes as input the features of adjacent
preceding scale for next-scale prediction, enabling the adoption of a parallel
training strategy that significantly reduces GPU memory consumption.
Furthermore, we propose spatial-Markov attention, which restricts the attention
of each token to a localized neighborhood of size k at corresponding positions
on adjacent scales, rather than attending to every token across these scales,
for the p