This August 2025 paper offers a comprehensive overview of diffusion language models (DLMs), contrasting them with traditional autoregressive (AR) and masked language models (MLMs). It highlights DLMs' unique advantages like parallel generation and iterative refinement, which address common AR model bottlenecks. The text also covers training methodologies for DLMs, including pre-training and fine-tuning techniques, as well as various inference strategies designed to enhance quality and efficiency. Furthermore, it explores the expansion of DLMs into multimodal applications and discusses current challenges and promising future research directions within the field.
Source:
https://arxiv.org/pdf/2508.10875
