arXiv 2610.31198 · cs.AI · 2026-10-22
Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models
Ofek Avivi, Eliron Rahimi, Avi Mendelson, Chaim Baskin
机构待核实
在最新榜 #362 本站收录于 2026-10-26
一句话要点
论文发现扩散语言模型的量化敏感度随任务和去噪步而变，提出无需标签的 T²Quant，在固定位宽预算下按去噪步分配激活精度，在 3 个模型 6 个基准上最多提升 6.7 个百分点。
Quantization sensitivity in diffusion language models varies by task and denoising step; label-free T²Quant allocates activation precision across steps under a fixed bit budget, improving accuracy by up to 6.7 points across three DLMs and six benchmarks.
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数据说明
元数据来自 arXiv API（已核实）；点赞来自 Hugging Face 每日论文；引用来自 Semantic Scholar。最近刷新 2026-10-26。
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