Visual AutoRegressive modeling (VAR) suffers from substantial computational cost due to the massive token count involved. Failing to account for the continuous evolution of modeling dynamics, existing VAR token reduction methods face three key limitations: heuristic stage partition, non-adaptive schedules, and limited acceleration scope, thereby leaving significant acceleration potential untapped. Since entropy variation intrinsically reflects the transition of predictive uncertainty, it offers a principled measure to capture modeling dynamics evolution. Therefore, we propose NOVA, a training-free token reduction acceleration framework for VAR models via entropy analysis. NOVA adaptively determines the acceleration activation scale during inference by online identifying the inflection point of scale entropy growth. Through scale-linkage and layer-linkage ratio adjustment, NOVA dynamically computes distinct token reduction ratios for each scale and layer, pruning low-entropy tokens while reusing the cache derived from the residuals at the prior scale to accelerate inference and maintain generation quality. Extensive experiments and analyses validate NOVA as a simple yet effective training-free acceleration framework.
FasterVAR: Plug-and-Play Acceleration for Visual Autoregressive Models
PCA Lab, VCIP, College of Computer Science, Nankai University（南开大学计算机学院、VCIP、PCA实验室）；Program of Computer Science, City University of Hong Kong (Dongguan), China（香港城市大学（东莞）计算机系，中国）；City University of Hong Kong, HK SAR, China（香港城市大学，香港特别行政区，中国）；Mohamed bin Zayed University of Artificial Intelligence, UAE（阿联酋Mohamed bin Zayed人工智能大学）；Linkoping University, Sweden（林地平大学，瑞典）；PCA Lab, School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院、PCA实验室）；College of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware Skipping
University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong University of Science and Technology（香港科技大学）；Central South University（中南大学）；National University of Singapore（新加坡国立大学）；Shanghai Jiaotong University（上海交通大学）
SJD++: Improved Speculative Jacobi Decoding for Training-free Acceleration of Discrete Auto-regressive Text-to-Image Generation
The University of Hong Kong（香港大学）；Huawei Noah’s Ark Lab（华为诺亚实验室）；Peking University（北京大学）；xAI；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）
SparVAR: Exploring Sparsity in Visual AutoRegressive Modeling for Training-Free Acceleration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Nanjing University of Science and Technology（南京理工大学）；City University of Hong Kong（香港城市大学）
ToProVAR: Efficient Visual Autoregressive Modeling via Tri-Dimensional Entropy-Aware Semantic Analysis and Sparsity Optimization
School of Computer Science, Peking University（北京大学计算机科学学院）；School of Electronics Engineering and Computer Science, Peking University（北京大学电子工程与计算机科学学院）；National Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理国家重点实验室）
Drift-AR: Single-Step Visual Autoregressive Generation via Anti-Symmetric Drifting
University of Science and Technology of China（中国科学技术大学）；The Chinese University of Hong Kong, MMLAB（香港中文大学多媒体实验室）；JD Explore Academy（京东探索研究院）；Beihang University（北京航空航天大学）
Wavefront Parallelization for Efficient Learned Image Compression
School of Fundamental Science（基础科学学校）；Engineering Waseda University Tokyo, Japan（东京日本早稻田大学工程）；School of Engineering Institute of Science Tokyo Tokyo, Japan（东京东京科学研究所工程学校）
Continuous Speculative Decoding for Autoregressive Image Generation
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统实验室）；JD.COM Inc（京东集团股份有限公司）；China Tower Corporation Limited（中国铁塔股份有限公司）
Fast Inference of Visual Autoregressive Model with Adjacency-Adaptive Dynamical Draft Trees
School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（东南大学新一代人工智能技术及交叉应用重点实验室（教育部））；New Laboratory of Pattern Recognition (NLPR), State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences (CASIA)（模式识别新实验室（NLPR），多模态人工智能系统国家重点实验室（MAIS），中国科学院自动化研究所（CASIA））；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Computing and Information Systems, Singapore Management University（新加坡国立大学计算机与信息学院）
