aiqbench analysis
This arXiv paper studies how to quantize diffusion language models, where sensitivity to reduced precision varies across both downstream tasks and denoising steps. It proposes T²Quant, which allocates activation precision per step using a label-free signal under a fixed bit budget, and reports accuracy gains of up to 6.7 points over existing methods. For practitioners, the results are benchmark-based on three models and six tasks, so they show a promising direction for efficient inference rather than a deployment-ready solution.
Different Tasks, Different Timesteps: Temporal Task-Aware Quantization for Diffusion Language Models
arXiv:2610.31198v1 Announce Type: new Abstract: 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.
Abstract: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.
Artificial Intelligence (cs.AI)
arXiv:2610.31198 [cs.AI]
(or arXiv:2610.31198v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.31198 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 214 words
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