Vector Quantization

Also known as VQ

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8 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 86

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Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models
May 14, 2026Senne Deproost, Denis Steckelmacher, Ann NowéReinforcement Learning Vector Quantization
Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium
XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference
May 14, 2026Thomas WittLLM Quantization Vector Quantization
Gemini Stiftung Leipzig
Asymmetric Generative Recommendation via Multi-Expert Projection and Multi-Faceted Hierarchical Quantization
May 14, 2026Bin Huang, Xin Wang, Junwei Pan +6Vector Quantization Recommender Systems
DCST, Tsinghua University Beijing, China · DCST, BNRist, Tsinghua University Beijing, China · Tencent Shenzhen, China
RQ-MoE: Residual Quantization via Mixture of Experts for Efficient Input-Dependent Vector Compression
May 14, 2026Zhengjia Zhong, Shuyan Ke, Zaizhou Lin +3Residual VQ Vector Quantization
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China.
Provable Quantization with Randomized Hadamard Transform
May 13, 2026Ying Feng, Piotr Indyk, Michael Kapralov +2Vector Quantization Randomized Sketching
ArcVQ-VAE: A Spherical Vector Quantization Framework with ArcCosine Additive Margin
May 13, 2026Jaeyung Kim, YoungJoon YooVariational Autoencoders Image Generation
Department of Artificial Intelligence, Chung-Ang University, Seoul, Republic of Korea · SNUAILAB, Seoul, Republic of Korea
FibQuant: Universal Vector Quantization for Random-Access KV-Cache Compression
May 12, 2026Namyoon Lee, Yongjune KimKV Caching Rotation-Based Quantization
POSTECH
Fitting Multilinear Polynomials for Logic Gate Networks
May 9, 2026Youngsung KimBoolean Function Learning Neural Network Optimization
Department of AI; and Department of ECE, Inha University
Continuous First, Discrete Later: VQ-VAEs Without Dimensional Collapse
May 7, 2026Xinyu Zhao, Nikita Karagodin, Hamed Hassani +3Variational Autoencoders Rate-Distortion Theory
MIT
Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
May 7, 2026Bowen Zheng, Weijian Luo, Guang Yang +2Visual Autoregressive Models Vector Quantization
The Chinese University of Hong Kong, Shenzhen · hi-Lab, Xiaohongshu Inc
Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven
May 7, 2026Ran Ben-Basat, William Kuszmaul, Michael Mitzenmacher +2Rotation-Based Quantization Gradient Compression
UCL and Broadcom · Carnegie Mellon University · Harvard University +2
Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing
Apr 30, 2026Silvio Martinico, Franco Maria Nardini, Cosimo Rulli +1Clustering Vector Quantization
University of Pisa & ISTI–CNR · Pisa, Italy · ISTI–CNR +1
Efficient VQ-QAT and Mixed Vector/Linear quantized Neural Networks
Apr 25, 2026Terry Gou, Puneet GuptaQuantization-Aware Training Vector Quantization
FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
Apr 22, 2026Ye Qiao, Yian Wang, Zhiheng Chen +2LLM Compression GPU Acceleration
University of California, Irvine Irvine, California, USA
Revisiting RaBitQ and TurboQuant: A Symmetric Comparison of Methods, Theory, and Experiments
Apr 21, 2026Jianyang Gao, Yutong Gou, Yuexuan Xu +5Vector Quantization KV-Cache Quantization
ETH Zurich · Nanyang Technological University · University of Michigan +2
A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Apr 20, 2026Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson +3Residual VQ Vector Quantization
UCL and Broadcom · VMware Research by Broadcom · North Carolina State University +2
AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization
Apr 20, 2026Kosuke Matsushima, Yasuyuki Okoshi, Masato Motomura +1Compute-in-Memory Long-Context Language Model Inference
Institute of Science Tokyo†
A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics
Apr 16, 2026Fawad Javed Fateh, Ali Shah Ali, Murad Popattia +4ICL for Robotics Vector Quantization
Retrocausal, Inc., Redmond, WA 98052, USA
Unsupervised Skeleton-Based Action Segmentation via Hierarchical Spatiotemporal Vector Quantization
Apr 16, 2026Umer Ahmed, Syed Ahmed Mahmood, Fawad Javed Fateh +3Temporal Action Segmentation Vector Quantization
Retrocausal, Inc. Redmond, WA
Leech Lattice Vector Quantization for Efficient LLM Compression
Mar 11, 2026Tycho F. A. van der Ouderaa, Mart van Baalen, Paul Whatmough +1LLM Quantization LLM Compression
Qualcomm AI Research
LGQ: Learnable Geometric Quantization for Image Tokenization
Feb 17, 2026Idil Bilge Altun, Mert Onur Cakiroglu, Elham Buxton +2Image Tokenization Vector Quantization
Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington · Department of Computer Science, University of Illinois Springfield · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
Pushing Toward the Simplex Vertices: A Simple Remedy for Code Collapse in Smoothed Vector Quantization
Sep 26, 2025Takashi MoritaLearned Image Compression Vector Quantization
Academy of Emerging Sciences, Chubu University · Institute for Advanced Research, Nagoya University
Discrete Tokenization for Multimodal LLMs: A Comprehensive Survey
Jul 21, 2025Jindong Li, Yali Fu, Jiahong Liu +5Multimodal Large Language Models Vector Quantization
Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China · Jilin University, Changchun, China · The Chinese University of Hong Kong, Hong Kong, China +2
NSNQuant: A Double Normalization Approach for Calibration-Free Low-Bit Vector Quantization of KV Cache
May 23, 2025Donghyun Son, Euntae Choi, Sungjoo YooKV Caching Vector Quantization
Seoul National University
LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression
Mar 25, 2023Xi Zhang, Xiaolin WuLearned Image Compression Vector Quantization
Shanghai Jiao Tong University · McMaster University
LC-QAT: Data-Efficient 2-Bit QAT for LLMs via Linear-Constrained Vector Quantization
Date pendingHaoyu Wang, Xingyu Yu, Haiyan Zhao +2LLM Quantization Quantization-Aware Training
Department of Computer Science and Technology, Tsinghua University, Beijing, China · School of Software and Microelectronics, Peking University, Beijing, China · College of Computer Science and Technology, National University of Defense Technology, Hunan, China.

May 14, 2026, cs.LGJ/K move · Enter open · S save

Critic-Driven Voronoi-Quantization for Distilling Deep RL Policies to Explainable Models

Senne Deproost, Denis Steckelmacher, Ann Nowé

Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

Reinforcement Learning Vector Quantization Interpretability in RL Policy Distillation Knowledge Distillation

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Despite many successful attempts at explaining Deep Reinforcement Learning policies using distillation, it remains difficult to balance the performance-interpretability trade-off and select a fitting surrogate model. In addition to this, traditional distillation only minimizes the distance between the behavior of the original and the surrogate policy while other RL-specific components such as action value are disregarded. To solve this, we introduce a new model-agnostic method called Critic-Driven Voronoi State Partitioning, which partitions a black box control policy into regions where a simple class of model can be optimized using gradient descent. By exploiting the critic value network of the original policy, we iteratively introduce new subpolicies in regions with insufficient value, standing in for a measure of policy complexity. The partitioning, a Voronoi quantizer, uses nearest neighbor lookups to assign a linear function to each point in the state space resulting in a cell-like diagram. We validate our approach on several well known benchmarks and proof that this distillation approaches the original policy using a reasonable sized set of linear functions.

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Latest papers 86