Diffusion Language Models: Foundations, Efficiency, and Reasoning

Autoregressive (AR) generation has dominated language modeling for years, but a fundamentally different paradigm is gaining rapid traction: diffusion language models (DLMs). Rather than generating tokens left-to-right, diffusion language models corrupt sequences through forward noising over categorical spaces and learn to reverse this corruption, enabling parallel decoding, bidirectional context, and fine-grained controllable generation. In 2025–2026, this paradigm transitioned from theoretical curiosity to commercial reality: Inception Labs launched Mercury, the first commercial-scale diffusion LLM; academic labs released LLaDA and Dream; and academic work—SEDD, MDLM, FS-DFM, LaViDa—has shown diffusion language models can match or exceed AR baselines while achieving up to 10× inference speedups. This full-day workshop brings together researchers from academia and industry to consolidate theoretical understanding, benchmark competing approaches, and chart a roadmap for diffusion language models, catalyzing collaborations across the generative modeling, NLP, and systems communities.