Diffusion language models (dLLMs) generate several tokens at once but usually throw away lots of helpful clues each stepβRCD keeps and reuses those clues.
The paper proposes Diffusion in Diffusion, a draft-then-revise method that brings back global coherence to fast, block-based diffusion language models.