不同任务，不同时间步：扩散语言模型的时序任务感知量化 T²Quant

Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models

要点速览

T²Quant 框架通过少量无标签校准提示预测精度敏感性，在固定比特宽预算下分配激活精度，在 3 个扩散语言模型和 6 个基准测试中改进现有量化方法达 6.7 个百分点，无需增加平均精度预算。

正文

Excited to share our new paper: Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models! 🚀

We reveal that quantization sensitivity is both task and time-dependent, with different downstream tasks exhibiting distinct precision requirements across denoising steps.

Building on this finding, we introduce T²Quant, a mixed-precision framework that:

🔍 Predicts precision sensitivity using only a few unlabeled calibration prompts.
⚙️ Allocates activation precision across denoising steps under a fixed bit-width budget.
📈 Improves existing quantization methods by up to 6.7 percentage points, without increasing the average precision budget.

Evaluated across 3 diffusion language models and 6 benchmarks.

📄 Paper: https://arxiv.org/abs/2610.31198

We welcome feedback, questions, and discussions!