arxiv:2610.31198

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

Published on Oct 22

· Submitted by

Eliron Rahimi on Oct 23

Upvote

Authors:

Eliron Rahimi ,

Abstract

T2Quant improves diffusion language model quantization by dynamically allocating activation precision across denoising steps based on predicted task- and time-dependent sensitivity.

Generated by thinkingmachines/Inkling-Small

Diffusion Language Models (DLMs) introduce unique quantization challenges due to their iterative denoising process. In this work, we reveal that quantization sensitivity is both taskand time-dependent, with different downstream tasks exhibiting distinct precision requirements across denoising steps. We further show that these patterns can be predicted without groundtruth labels or task-specific evaluation metrics. Building on these findings, we introduce T 2Quant, a task and time-aware mixedprecision quantization framework that uses a label-free sensitivity signal to optimally allocate activation precision across denoising steps under a fixed bit-width budget. Extensive experiments across three DLMs and six benchmarks demonstrate that T 2Quant consistently improves the average accuracy of existing quantization methods, achieving gains of up to 6.7 percentage points under the same budget.

View arXiv page View PDF Project page GitHub 3 Add to collection

Community

ElironRahimi

Paper author Paper submitter about 9 hours ago

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!