Unverified50% confidenceFactExact time
The 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.
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10/16/2026
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Related Claims
UnverifiedMasked 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.76% similarUnverifiedDuring MDM generation, the conditional distribution at each position can be computed simultaneously, which naturally supports parallel decoding, in sharp contrast to the way autoregressive models generate tokens one by one.72% similarUnverifiedBERT's MLM performs one-shot prediction and is primarily used for representation learning, whereas discrete diffusion models treat the masking ratio as a continuous variable from 0% to 100% to construct a complete Markov chain, achieving progressive generation through multi-step iteration.69% similarUnverifiedDiscrete-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.68% similarVerifiedThe denoising process of diffusion models is based on the DDPM principle, which involves progressively adding Gaussian noise to data during training and then training a neural network to learn the reverse denoising process.67% similar
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