Abstract
Block-wise discrete diffusion offers an attractive balance between parallel generation and causal dependency modeling, making it a promising backbone for vision-language modeling. However, its practical adoption has been limited by high training cost, slow convergence, and instability, which have so far kept it behind strong autoregressive (AR) baselines. We present \textbf\{SDAR-VL\}, the first systematic application of block-wise discrete diffusion to large-scale vision-language understanding (VLU), together with an *integrated framework for efficient and stable training*. This framework unifies three components: (1) \textbf\{Asynchronous Block-wise Noise Scheduling\} to diversify supervision within each batch; (2) \textbf\{Effective Mask Ratio Scaling\} for unbiased loss normalization under stochastic masking; and (3) a \textbf\{Progressive Beta Noise Curriculum\} that increases effective mask coverage while preserving corruption diversity. Experiments on 21 single-image, multi-imag