Diffusion Large Language Models (dLLMs) can write many parts of an answer at once, not just left to right like usual chatbots.
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.