Apple researchers developed Normalizing Trajectory Models to fix sampling errors in few-step diffusion generation. Unlike distillation methods, NTM uses conditional normalizing flows to maintain exact likelihood training. This approach combines shallow invertible blocks with deep parallel architectures.