Public articles linked to the same research event.
arXiv LiFT supervises each loop of a recurrent DiT toward a point on a straight path from the model's initial velocity estimate to the flow-matching target, so a trained checkpoint can run far more inference loops than its training depth; on class-conditional ImageNet, L/2 and XL/2 checkpoints that reallocate inference compute toward recurrence reach lower FID than larger dense DiTs with fewer parameters and less inference compute, while at B/2 the dense model remains stronger.
LiFT supervises each loop of a recurrent DiT toward a point on a straight path from the model's initial velocity estimate to the flow-matching target, so a trained checkpoint can run far more inference loops than its training depth; on class-conditional ImageNet, L/2 and XL/2 checkpoints that reallocate inference compute toward recurrence reach lower FID than larger dense DiTs with fewer parameters and less inference compute, while at B/2 the dense model remains stronger.
LiFT supervises each loop of a recurrent DiT toward a point on a straight path from the model's initial velocity estimate to the flow-matching target, so a trained checkpoint can run far more inference loops than its training depth; on class-conditional ImageNet, L/2 and XL/2 checkpoints that reallocate inference compute toward recurrence reach lower FID than larger dense DiTs with fewer parameters and less inference compute, while at B/2 the dense model remains stronger.
LiFT supervises each loop of a recurrent DiT toward a point on a straight path from the model's initial velocity estimate to the flow-matching target, so a trained checkpoint can run far more inference loops than its training depth; on class-conditional ImageNet, L/2 and XL/2 checkpoints that reallocate inference compute toward recurrence reach lower FID than larger dense DiTs with fewer parameters and less inference compute, while at B/2 the dense model remains stronger.