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Sequence recommendation models can achieve near-perfect scaling efficiency in distributed training, slashing wasted GPU cycles by up to 90%.
Autoregressive video diffusion models can achieve faster decoding, lower memory footprint, and higher quality long-horizon generations by learning to attend to only the most salient spatiotemporal blocks.
