Why Diffusion Language Models Struggle with Truly Parallel (Non-Autoregressive) Decoding?

Researchers identify why Diffusion Language Models (DLMs) struggle with parallel token generation, finding that training data structure forces autoregressive-like behavior. They propose NAP, a data-centric approach using multiple independent reasoning trajectories that improves parallel decoding performance on math benchmarks.