Continuous Diffusion Language Models (CDLM's)
Why CEREBRO kept it
Novel diffusion language model architecture, core LLM mechanics
The text below is an automated extraction of the article at https://sander.ai/2026/08/24/continuous-dlms.html, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (sander.ai).
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. The recent influx of new research in this space inspired me to write up some of my thoughts. I have written about diffusion language models before, so this mainly s
Community take
Autoregressive dominance was already established in 2020 (GPT-3 hype was real), contradicting the article's framing of encoder novelty.
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