Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models
The paper reports that quantization sensitivity in Diffusion Language Models depends on both the downstream task and the denoising timestep. It introduces T²Quant, a mixed-precision framework that uses a label-free sensitivity signal, estimated from a few unlabeled calibration prompts, to allocate activation precision across denoising steps under a fixed bit-width budget. Experiments across three DLMs and six benchmarks report gains of up to 6.7 percentage points over existing quantization methods under the same budget.
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- Primary sourceHugging Face Daily Papers
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper reports that quantization sensitivity in Diffusion Language Models depends on both the downstream task and the denoising timestep. It introduces T²Quant, a mixed-precision framework that uses a label-free sensitivity signal, estimated from a few unlabeled calibration prompts, to allocate activation precision across denoising steps under a fixed bit-width budget. Experiments across three DLMs and six benchmarks report gains of up to 6.7 percentage points over existing quantization methods under the same budget.
Key findings from the stored brief
- Quantization sensitivity in diffusion language models varies by downstream task and across denoising steps.
- The sensitivity patterns can be predicted without ground-truth labels or task-specific evaluation metrics.
- T²Quant allocates activation precision across denoising steps under a fixed bit-width budget.
- Across three DLMs and six benchmarks, T²Quant improves average accuracy of existing methods by up to 6.7 points.
Source claims not independently checked; the reported gains come from the authors' own experiments.
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Why this may matter
Diffusion language models are costly to run because of iterative denoising. Allocating precision unevenly across steps and tasks, without labeled data, could reduce compute or memory while preserving accuracy.
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Source claims not independently checked; the reported gains come from the authors' own experiments.
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