Unverified50% confidenceFactExact time
Discrete diffusion models construct complete sequences by iteratively and in parallel resolving multiple tokens across multiple steps, unlike the left-to-right word-by-word generation of autoregressive models.
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50%
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Long-term
Relevance
9/30/2026
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Related Claims
Unverified扩散模型通过全局并行生成和迭代修正，能从根本上规避自回归模型的「曝光偏差」（Exposure Bias）累积错误问题77% similarVerifiedDiffusion language models can simultaneously perform parallel prediction and iterative refinement on all tokens in the entire sequence, rather than outputting them one by one.77% similarUnverifiedDiffusion model inference requires a fixed number of forward passes with each pass acting on the entire sequence, making the total cost a function of the number of steps multiplied by the sequence length.76% 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.75% similar
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