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Prefix-Conditioned Diffusion (PCD), which samples a boundary, preserves the prefix, preserves the prefix, and denoises the suffix, is proposed, and improvements in reasoning and coding over native diffusion training are shown.
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.
This work systematically study weight-only post-training quantization across bit-widths, quantization methods, model scales and downstream tasks and establishes that quantization degradation is governed by how errors are introduced at the source and how they accumulate across the network.
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