FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
arXiv:2606.06547v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) refine tokens iteratively but commit them irreversibly, leading to a "stability lag" where early decisions remain fragile even after being written. We reveal that Post-Training Quantization (PTQ) error easily flips these borderline decisions at the write frontier, which are then permanently locked in and amplified. To address this, we propose Frontier-Aware Instability-Reweighted Calibration (FAIR-Calib), a two-stage PTQ framework for dLLMs. Stage I probes a full-precision teacher to estimate a position pri
The increasing scale and complexity of LLMs, especially diffusion models, are pushing the boundaries of efficient deployment, making quantization strategies critical for practical applications.
Improving the efficiency of large language models through advanced quantization techniques directly addresses the significant computational and energy costs associated with their development and deployment.
New calibration methods like FAIR-Calib could make quantized diffusion LLMs more reliable and performant, accelerating their adoption in resource-constrained environments.
- · AI developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · Inefficient LLM architectures
FAIR-Calib reduces the computational overhead of dLLMs, making them more accessible and cheaper to run.
Wider deployment of efficient dLLMs could accelerate the development of new AI applications and services.
Increased accessibility might lead to novel societal impacts as complex generative AI becomes pervasive even on less powerful devices, potentially altering information consumption and creation.
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Read at arXiv cs.LG