Diffusion language models (DLMs) have emerged as a compelling alternative to autoregressive (AR) models by enabling parallel, non-sequential token generation. Despite their remarkable empirical success, the theoretical foundations of these models remain relatively underexplored, particularly in terms of convergence guarantees. In this talk, I will present a sharp convergence theory for both continuous and discrete diffusion models. In this talk, we present recent theoretical convergence guarantees for both discrete and continuous DLMs. These results provide a theoretical foundation for efficient diffusion-based language generation and offer principled insights into decoding strategy design.

Bio: Gen Li is currently an assistant professor in the Department of Statistics and Data Science at the Chinese University of Hong Kong. His research interests include diffusion model, generative AI and reinforcement learning.