Volodymyr Kuleshov 🇺🇦@volokuleshovTECH
New blog post: How to Build a Diffusion Language Model. Diffusion LLMs went from open problem to reality in 2 years (Mercury, Gemma Diffusion, Nemotron Diffusion). With my Cornell group, we wrote up the research advances that make them work. We use today's OSS large diffusion models as examples and show how they're built from a core techniques: • masked diffusion (MDLM) • iterative refinement (UDLM, ReMDM) • variable-length generation (block diffusion, encoder-decoder architectures) • controllable generation (D-CFG, D-CBG) • fast samplers (Duo) • RL post-training (d1, d2) This post covers research led by a talented group of Cornell PhD students including @mariannearr @SchiffYair @Guanghan__Wang The content is adapted from talks I gave this year on dLLMs. It covers most of the main ingredients behind open-source diffusion language models today. Link: https://kuleshov-group.github.io/blog/blog/2026/how-to-build-a-diffusion-language-model/