Ning Zhang, Shihao Wang, Jinrui Zhang, Chaodong Xiao, Lei Zhang · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28500 · open-access PDF
For efficient deployment of large language models (LLMs), methods of ultra-low-bit backbones reduce weight storage, memory traffic, and linear-layer execution cost, while multi-token prediction (MTP) methods reduce sequential decoding steps by predicting multiple future tokens in parallel. Although complementary, the direct combination of these methods cannot accelerate decoding at comparable quality: masked drafting lacks intermediate token input supplied in next-token training, while wider computation and causal key-value (KV) updates can offset the savings from fewer steps. To address these
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