Unverified50% confidenceFactExact time
In the forward process, masked diffusion replaces any token with the [MASK] token at a certain rate, with the final state being a fully masked sequence, and its reverse process aligns closely with the BERT-style masked language model training objective.
1
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50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
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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.76% similarUnverifiedMasked 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.73% similarUnverifiedMasked diffusion randomly masks some tokens in the sequence during training, and during generation starts from the masked state and gradually denoises through multiple iterations to restore the complete sequence, with each step able to predict tokens at multiple positions in parallel.73% similarUnverifiedDiscrete diffusion models specifically handle discrete data (such as amino acid sequences, DNA sequences, or text); the forward process typically employs a masking mechanism that replaces certain positions in the sequence with [MASK] tokens, which the model progressively predicts and restores during the reverse process.68% similarUnverifiedThe noising process of a masked diffusion model (MDM) progressively replaces tokens in the sequence with a special [MASK] token, and the training objective is for the model to learn to predict the original token at each masked position based on the currently partially visible sequence.68% similar
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