Continuous Diffusion Language Models (CDLMs)

Sander Dieleman's post covers continuous diffusion language models (CDLMs), which apply continuous-domain diffusion to discrete text by embedding tokens in high-dimensional space. The approach sidesteps the sequential bottleneck of autoregressive generation and enables parallel decoding. The post includes benchmark comparisons against masked and autoregressive baselines on standard NLP tasks.