Remask, Don't Replace: Token-to-Mask Refinement in Masked Diffusion Language Models

This paper proposes a novel technique, Token-to-Mask (T2M) remasking, to refine masked diffusion language models like LLaDA2.1. The method addresses the shortcomings of Token-to-Token (T2T) editing by resetting suspect tokens to a mask state, enabling more accurate re-prediction.