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A fine-grained comparison of the capacity for parallelism among these three leading approaches is initiated and it is proved that uniform and Gaussian diffusion can sample in a number of forward passes which scales with the dual total correlation of the underlying distribution.
A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more efficiently than autoregressive models, which require one forward pass per token. Yet among the many competing paradigms for dLLMs, from masked to uniform to Gaussian...
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