Copertina

Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Lin, Haokun, Xu, Haobo, Wu, Yichen, Guo, Ziyu, Zhang, Renrui, Lu, Zhichao, Wei, Ying, Zhang, Qingfu, Sun, Zhenan
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Computation and Language Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
di: Lin, Haokun, et al.
Pubblicazione: (2026)

DuQuant: Distributing Outliers via Dual Transformation Makes Stronger Quantized LLMs
di: Lin, Haokun, et al.
Pubblicazione: (2024)

DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
di: Yang, Lianwei, et al.
Pubblicazione: (2024)

LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation
di: Yang, Lianwei, et al.
Pubblicazione: (2025)

dVoting: Fast Voting for dLLMs
di: Feng, Sicheng, et al.
Pubblicazione: (2026)

DMax: Aggressive Parallel Decoding for dLLMs
di: Chen, Zigeng, et al.
Pubblicazione: (2026)

TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
di: Lin, Haokun, et al.
Pubblicazione: (2025)

dParallel: Learnable Parallel Decoding for dLLMs
di: Chen, Zigeng, et al.
Pubblicazione: (2025)

dMoE: dLLMs with Learnable Block Experts
di: Feng, Sicheng, et al.
Pubblicazione: (2026)

Every Step Counts: Decoding Trajectories as Authorship Fingerprints of dLLMs
di: Li, Qi, et al.
Pubblicazione: (2025)

BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
di: Huang, Wei, et al.
Pubblicazione: (2024)

STDec: Spatio-Temporal Stability Guided Decoding for dLLMs
di: Chen, Yuzhe, et al.
Pubblicazione: (2026)

EDIT: Early Diffusion Inference Termination for dLLMs Based on Dynamics of Training Gradients
di: Hsieh, He-Yen, et al.
Pubblicazione: (2025)

CRVQ: Channel-Relaxed Vector Quantization for Extreme Compression of LLMs
di: Xu, Yuzhuang, et al.
Pubblicazione: (2024)

Towards Accurate Post-training Quantization for Diffusion Models
di: Wang, Changyuan, et al.
Pubblicazione: (2023)

Towards Accurate Post-training Quantization for Reparameterized Models
di: Zhang, Luoming, et al.
Pubblicazione: (2024)

QVD: Post-training Quantization for Video Diffusion Models
di: Tian, Shilong, et al.
Pubblicazione: (2024)

D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs
di: Yan, Xianglong, et al.
Pubblicazione: (2026)

PQD: Post-training Quantization for Efficient Diffusion Models
di: Ye, Jiaojiao, et al.
Pubblicazione: (2024)

A Systematic Evaluation of On-Device LLMs: Quantization, Performance, and Resources
di: Song, Qingyu, et al.
Pubblicazione: (2025)

SliderQuant: Accurate Post-Training Quantization for LLMs
di: Wang, Shigeng, et al.
Pubblicazione: (2026)

Achieving binary weight and activation for LLMs using Post-Training Quantization
di: Song, Siqing, et al.
Pubblicazione: (2025)

PTQ4DiT: Post-training Quantization for Diffusion Transformers
di: Wu, Junyi, et al.
Pubblicazione: (2024)

What Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Study
di: Lv, Keyu, et al.
Pubblicazione: (2026)

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
di: Ouyang, Xu, et al.
Pubblicazione: (2024)

QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
di: Zhang, Jingxuan, et al.
Pubblicazione: (2026)

Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models
di: Zhang, Tianao, et al.
Pubblicazione: (2025)

Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs
di: Xu, Zifei, et al.
Pubblicazione: (2024)

Sensitivity-Aware Post-Training Quantization for Deep Neural Networks
di: Zheng, Zekang, et al.
Pubblicazione: (2025)

Integer Scale: A Free Lunch for Faster Fine-grained Quantization of LLMs
di: Li, Qingyuan, et al.
Pubblicazione: (2024)

Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
di: Zhao, Jiaqi, et al.
Pubblicazione: (2025)

Selective Focus: Investigating Semantics Sensitivity in Post-training Quantization for Lane Detection
di: Fan, Yunqian, et al.
Pubblicazione: (2024)

LightningRL: Breaking the Accuracy-Parallelism Trade-off of Block-wise dLLMs via Reinforcement Learning
di: Hu, Yanzhe, et al.
Pubblicazione: (2026)

Pyramid Vector Quantization for LLMs
di: van der Ouderaa, Tycho F. A., et al.
Pubblicazione: (2024)

Interpreting the Effects of Quantization on LLMs
di: Singh, Manpreet, et al.
Pubblicazione: (2025)

DAQ: Density-Aware Post-Training Weight-Only Quantization For LLMs
di: Luo, Yingsong, et al.
Pubblicazione: (2024)

CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
di: Wang, Haoyu, et al.
Pubblicazione: (2024)

Evaluating the Impact of Post-Training Quantization on Reliable VQA with Multimodal LLMs
di: Kurz, Paul Jonas, et al.
Pubblicazione: (2026)

BiDM: Pushing the Limit of Quantization for Diffusion Models
di: Zheng, Xingyu, et al.
Pubblicazione: (2024)

Inducing Systematicity in Transformers by Attending to Structurally Quantized Embeddings
di: Jiang, Yichen, et al.
Pubblicazione: (2024)