diffusion-language-models
5 free lessons tagged diffusion-language-models across AI. Each one is a short sequence of focused steps with narration and a five-question quiz at the end — take them in any order, no signup required.
Beyond Text: Multimodal, Safety, and Open Problems
Where discrete diffusion goes after language: unified multimodal models, sequence design in biology, the fixed-length problem nobody has cleanly solved, and why alignment and evaluation both need rebuilding.
Making Diffusion LLMs Actually Fast
Why bidirectional attention breaks the KV cache, how block-wise approximate caching brings it back, and the conditional-independence problem that decides how many tokens you can safely unmask at once.
Training Diffusion Language Models at Scale
What it takes to train a diffusion language model to billions of parameters: the from-scratch recipe, adaptation from an autoregressive checkpoint, the supervision economics, and the capability that bidirectional training gets for free.
The Mathematics of Discrete Diffusion
The formal machinery under masked text generation: categorical transition matrices, the kernel choices that were tried and discarded, the variational bound, and the two parameterizations that made the objective trainable at scale.
Diffusion language models: generating text all at once
How diffusion was rebuilt for discrete text: masked diffusion instead of Gaussian noise, parallel refinement instead of token-by-token decoding, the speed economics that make it attractive, and the honest trade-offs against autoregressive transformers.