Contents

Masked Diffusion in a Nutshell

Understanding Masked Diffusion: A Probabilistic Perspective

Block Diffusion for Flexible-Length Generation

Architectures: Encoder, Decoder, and Encoder-Decoder

Iterative Refinement and Built-In Error Correction

Accelerating Diffusion Sampling via Distillation

Diffusion Enables Controllable Generation

Post-Training Diffusion Language Models

Biological and Scientific Domains

Diffusion-Based Large Language Models

Is Diffusion A Path Towards More Intelligent Models? A Scaling-Law Perspective

Introduction: Autoregressive and Diffusion Language Models

Two families of generative AI algorithms are widely used today. For continuous data such as images or video, the state-of-the-art approach is based on diffusion models . For discrete data such as text or code, the standard approach is instead autoregressive models .…