Unverified50% confidenceFactExact time
Masked Diffusion Language Modeling (MDLM) operates on discrete token spaces, where the forward process randomly replaces tokens with [MASK] and the reverse process trains the model to progressively restore the masked tokens.
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50%
Confidence
Long-term
Relevance
10/7/2026
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Related Claims
UnverifiedDiscrete-space diffusion defines the diffusion process directly on token sequences by introducing a [MASK] token; the forward process progressively replaces real tokens with masks, and the reverse process learns to predict the original masked tokens.73% similarVerifiedGLM uses an autoregressive blank-filling training objective, randomly masking text segments and requiring the model to autoregressively restore them.71% similarUnverifiedBuilding a minimum viable diffusion language model can adopt the discrete mask diffusion approach, referencing the MDLM or D3PM framework, where the forward process replaces tokens with [MASK] at timestep-dependent probabilities via a transition matrix.71% similarUnverified离散扩散语言模型主流方案包括：将token映射到连续嵌入空间进行扩散（如Diffusion-LM），或设计离散扩散过程用掩码模拟加噪（如D3PM、MDLM）70% similarUnverifiedRepresentative discrete diffusion works include the Masked Diffusion Language Model (MDLM) and its subsequent improvements, and have been applied to the generation of structured data such as code and protein sequences.70% similar
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