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Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough performance in frontier production models, but their effectiveness deteriorates as the entropy of target distributions increases due to insuffic...
Diffusion language models (dLLMs) generate text through iterative denoising, allowing multiple tokens to be predicted in parallel. However, pretraining may mask tokens throughout a sequence, whereas prompt continuation conditions on an intact prefix. This difference remains in conversion pipelines that denoise entire s...
This work systematically characterize how optimization hyperparameters, compute allocation, and architecture scale for MoE dLLMs, identifying quantitative differences from scaling trends previously reported for AR models.
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