VoidPadding: Let [VOID] Handle Padding in Masked Diffusion Language Models so that [EOS] Can Focus on Semantic Termination

MDLMs generate text by denoising a preallocated masked response canvas, making response-length modeling central to instruction tuning. Existing MDLMs often inherit the autoregressive convention of using repeated [EOS] tokens for padding during instruction tuning, giving [EOS] a dual role as both a semantic terminator and a padding token. We show that this dual role is a root cause of [EOS] overflow under large-block decoding. To decouple these roles, we propose VoidPadding, which introduces [VOID] for padding and reserves [EOS] for termination. During inference, the learned [EOS] signal enables early stopping, while the learned [VOID] signal guides adaptive response canvas expansion. On Dream-7B-Instruct, VoidPadding improves the block-size-averaged four-task mean across mathematical reasoning and code generation benchmarks by (+17.84) points over the original model and (+6.95) points over RainbowPadding, while reducing decoding NFE by 55.7\% on average.