Continuous diffusion language models
Summary
The article surveys the revival of continuous diffusion language models (CDLMs), explaining how CDLMs differ from discrete diffusion approaches, and outlining embedding strategies, loss functions, and noise schedules. It discusses self-conditioning, hybrid discrete-continuous methods, and emerging techniques like flow maps, while highlighting evaluation challenges and directions for future work.