Continuous Diffusion Language Models (CDLM's)
This article examines the resurgence of continuous diffusion models for language, contrasting them with the dominant autoregressive Transformer architecture. It provides a historical and technical overview of why researchers are revisiting this approach.
Why it matters
Understanding alternative architectures to Transformers is vital for the future of AI scalability and the development of more efficient generative models.
A flurry of recent activity in the space of continuous diffusion models for language , after a few years of relative dormancy, suggests that this approach is making something of a comeback. Fully discrete diffusion methods had largely supplanted earlier attempts to make continuous diffusion work for language, but the tide is starting to turn. In this post, I want to take a closer look at what’s going on, and why it is happening now.
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