“Discrete Beckmann Transport Models for One-Step Language Modeling and Reasoning” Few-step diffusion and flow LMs usually require distilling a pretrained teacher, which adds training cost and caps the student at teacher quality. So this paper learns a direct path from noise to discrete tokens, generating the whole sequence in one step without a teacher model or tracking diffusion time. Then when you add extra steps, the model doesn’t just take smaller steps toward an answer. It revisits…