arXiv Machine Learning June 4, 2026 By Xin Yan, Aqiang Wang, Zhenglin Wan, Xingrui Yuand Ivor Tsang

STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models

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arXiv:2606. 04945v1 Announce Type: new Abstract: Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirectional context.

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Diffusion large language models (DLLMs) have recently emerged as a promising alternative to autoregressive LLMs by generating text through iterative masked denoising with bidirectional context. However, their large model sizes and iterative denoising process introduce substantial memory and computational overhead, motivating post-training quantization for efficient deployment.

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