Text Diffusion Models Show Promise For Gap-Filling

Researchers and developers are revisiting text diffusion models, with recent papers such as LLaDA and SEDD demonstrating token-masking diffusion for discrete text generation. These models enable parallel masked-token denoising, show faster throughput on long outputs (e.g., LLaDA 2.0 Flash >380 TPS) and suit gap-filling and structured editing workflows, while still lagging autoregressive models on broad benchmarks and production readiness.