LLM Quantization

LLM: Large Language Model

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33 papers in the last four weeks, up 43% on the four weeks before. 0.3% of all new papers.

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WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
May 17, 2026Dongyue Li, Zechun Liu, Kai Yi +6LLM Quantization Quantization-Aware Training
1Northeastern University, MA · 2Meta AI, CA.
Measuring Maximum Activations in Open Large Language Models
May 15, 2026Luxuan Chen, Han Tian, Xinran Chen +9LLM Quantization Activation Outliers
1Shanghai Jiao Tong University · 2Baidu Inc. · 4Nankai University +1
Widening the Gap: Exploiting LLM Quantization via Outlier Injection
May 14, 2026Xiaohua Zhan, Kazuki Egashira, Robin Staab +2LLM Quantization LLM Backdoor Attacks
ETH Zurich
A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models
May 14, 2026Earl KillianMixed-Precision Quantization LLM Quantization
XFP: Quality-Targeted Adaptive Codebook Quantization with Sparse Outlier Separation for LLM Inference
May 14, 2026Thomas WittLLM Quantization Vector Quantization
Gemini Stiftung Leipzig
High-Rate Quantized Matrix Multiplication II
May 13, 2026Or Ordentlich, Yury PolyanskiyLLM Quantization Rate-Distortion Theory
the Hebrew University of Jerusalem, Israel · MIT, USA
Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference
May 13, 2026Lingchao Zheng, Yuwei Fan, Jun Li +5LLM Quantization LLM Inference Acceleration
Huawei
Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
May 12, 2026Vage Egiazarian, Erik Schultheis, Andrei Panferov +3LLM Quantization 4-Bit Quantization
ISTA · Aril Computer · ETH Zürich +1
SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization
May 12, 2026Chengzhu Bao, Xianglong Yan, Zhiteng Li +3LLM Quantization 4-Bit Quantization
Shanghai Jiao Tong University · Tencent Hunyuan
PRISM: A Geometric Risk Bound for Decomposing Drift into Scale, Shape, and Head
May 12, 2026Chieh-Yen Lin, Shao-Hua SunLLM Quantization LLM Evaluation
Appier AI Research · National Taiwan University
Efficient LLM-based Advertising via Model Compression and Parallel Verification
May 12, 2026Wenxin Dong, Chang Gao, Guanghui Yu +9LLM Quantization LLM Compression
Baidu Inc. Beijing, China
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
May 11, 2026Ryan Lucas, Mehdi Makni, Xiang Meng +2LLM Quantization Alternating Direction Method of Multipliers
MIT Operations Research Center · MIT Sloan School of Management · MIT Center for Statistics
Compander-Aligned Query Geometry for Quantized Zeroth-Order Optimization
May 11, 2026Yao Shu, Zilin ZhuLLM Quantization Zeroth-Order Optimization
HKUST-GZ
BCJR-QAT: A Differentiable Relaxation of Trellis-Coded Weight Quantization
May 11, 2026Venugopalan IyengarLLM Quantization Quantization-Aware Training
Theory-optimal Quantization Based on Flatness
May 11, 2026Xiusheng Huang, Zhe Li, Xuanwu Yin +5LLM Quantization Activation Quantization
The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China · School of Artificial Intelligence, University of Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence +2
Pretraining large language models with MXFP4 on Native FP4 Hardware
May 11, 2026Musa Cim, Poovaiah Palangappa, Miro Hodak +3Language Model Pretraining LLM Quantization
1The Pennsylvania State University · 2Advanced Micro Devices, Inc.
Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs
May 9, 2026Yuzhuang Xu, Xu Han, Yuxuan Li +2LLM Quantization Post-Training Quantization
1Harbin Institute of Technology, Harbin, China · 2Tsinghua University, Beijing, China
LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss
May 9, 2026Euntae Choi, Sumin Song, Sungjoo YooLLM Quantization Quantization-Aware Training
Seoul National University
AAAC: Activation-Aware Adaptive Codebooks for 4-bit LLM Weight Quantization
May 9, 2026Beshr IslamBouli, David JinLLM Quantization 4-Bit Quantization
Massachusetts Institute of Technology, Cambridge, MA, USA.
Finer is Better (with the Right Scaling)
May 8, 2026Clemens Schaefer, Gil TabakLLM Quantization 4-Bit Quantization
Google LLC, Mountain View, Ca
Litespark Inference For CPUs: Ultra-Fast SIMD Framework for Ternary (1.58-bit) Language Models
May 7, 2026Nii Osae Osae Dade, Tony Morri, Moinul Hossain Rahat +2LLM Quantization Efficient Neural Network Inference
Mindbeam AI
Normalized Architectures are Natively 4-Bit
May 7, 2026Maxim Fishman, Brian Chmiel, Ron Banner +2LLM Quantization Neural Representation Geometry
∧NVIDIA · ◦Department of Electrical and Computer Engineering - Technion, Haifa, Israel
Saliency-Aware Regularized Quantization Calibration for Large Language Models
May 7, 2026Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu +6LLM Quantization Language Model Calibration
University of Science and Technology of China · University College London · 3Shanghai University of Finance and Economics +5
OSAQ: Outlier Self-Absorption for Accurate Low-bit LLM Quantization
May 6, 2026Zhikai Li, Zhen Dong, Xuewen Liu +2LLM Quantization Weight-Only Quantization
Institute of Automation, Chinese Academy of Sciences · University of California, Santa Barbara.
QuIDE: Mastering the Quantized Intelligence Trade-off via Active Optimization
May 5, 2026Xiantao JiangMixed-Precision Quantization LLM Quantization
College of Information Engineering, Shanghai Maritime University Shanghai 201306, China
Statistically-Lossless Quantization of Large Language Models
May 4, 2026Michael Helcig, Eldar Kurtic, Dan AlistarhLLM Quantization LLM Inference Acceleration
ETH Z̈urich · ISTA
Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels
May 2, 2026Plawan Kumar Rath, Rahul MaliakkalLLM Quantization Social Bias in Language Models
Meta
AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs
May 1, 2026Wenxiang Lin, Juntao Huang, Luhan Zhang +5LLM Quantization Communication-Efficient Distributed Training
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen · Huawei Technologies Ltd.
Silicon Showdown: Performance, Efficiency, and Ecosystem Barriers in Consumer-Grade LLM Inference
May 1, 2026Abdurrahman Javat, Allan KazakovLLM Inference Efficiency LLM Quantization
Bahcesehir University, Istanbul, Türkiye
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
May 1, 2026Zhixiong Zhao, Zukang Xu, Dawei YangLLM Quantization LLM Inference Acceleration
1Houmo AI
Technical Report: Activation Residual Hessian Quantization (ARHQ) for Low-Bit LLM Quantization
Apr 30, 2026YiFeng Wang, Zhun Sun, Keisuke SakaguchiLLM Quantization Activation Quantization
Graduate School of Information Sciences Tohoku University
CoQuant: Joint Weight-Activation Subspace Projection for Mixed-Precision LLMs
Apr 29, 2026Zhe Ding, Su Pan, Duowei PanMixed-Precision Quantization LLM Quantization
School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing, China · Amazon AGI, Seattle, WA, USA
Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2
Apr 28, 2026Chirag ShindeModel Compression LLM Quantization
Independent Researcher
BitRL: Reinforcement Learning with 1-bit Quantized Language Models for Resource-Constrained Edge Deployment
Apr 27, 2026Md. Ashiq Ul Islam Sajid, Mohammad Sakib Mahmood, Md. Tareq Hasan +3LLM Quantization RL for Language Models
Department of Computer Science BRAC University Dhaka 1212, Bangladesh · Department of Computer Science Missouri State University Springfield, MO, USA · Department of Computer Science & Engineering Prime University Dhaka, Bangladesh +2
Coverage-Based Calibration for Post-Training Quantization via Weighted Set Cover over Outlier Channels
Apr 27, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaLLM Quantization Activation Outliers
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
QuantClaw: Precision Where It Matters for OpenClaw
Apr 24, 2026Manyi Zhang, Ji-Fu Li, Zhongao Sun +5Mixed-Precision Quantization LLM Quantization
Huawei Technologies · National University of Singapore · University of Science and Technology of China
RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory
Apr 22, 2026Fei Zuo, Zikang Zhou, Hao Cong +2LLM Inference Efficiency Mixed-Precision Quantization
1BA TechWorks (BMW Group) · 2National University of Singapore · 3Tsinghua University
From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
Apr 21, 2026Chenxi Zhou, Pengfei Cao, Jiang Li +4LLM Quantization Post-Training Quantization
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences · The Key Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences · College of Computer Science, Inner Mongolia University +1
LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
Apr 21, 2026Siqing Song, Chuang Wang, Yong Lang +2LLM Quantization Activation Quantization
MAIS, Institute of Automation, Chinese Academy of Sciences, China · School of Artificial Intelligence, University of Chinese Academy of Sciences, China · Central Media Technology Institute, Huawei
Benchmarking System Dynamics AI Assistants: Cloud Versus Local LLMs on CLD Extraction and Discussion
Apr 20, 2026Terry LeitchLLM Quantization Dynamical Systems
Ruxton AI
GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling
Apr 20, 2026Alireza Dadgarnia, Soroush Tabesh, Mahdi Nikdan +4LLM Quantization Post-Training Quantization
ISTA · ETH Zürich · ISTA & Red Hat AI +1
Depth Registers Unlock W4A4 on SwiGLU: A Reader/Generator Decomposition
Apr 20, 2026Ziyang LiuLLM Quantization 4-Bit Quantization
Independent Researcher
DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
Apr 20, 2026Haokun Lin, Xinle Jia, Haobo Xu +7LLM Quantization 4-Bit Quantization
CASIA · CityU · NJU +3
D-QRELO: Training- and Data-Free Delta Compression for Large Language Models via Quantization and Residual Low-Rank Approximation
Apr 18, 2026Junlin Li, Shuangyong Song, Guodong Du +6LLM Quantization LLM Compression
Harbin Institute of Technology, Shenzhen, China · TeleAI of China Telecom · The Hong Kong Polytechnic University +1
When Flat Minima Fail: Characterizing INT4 Quantization Collapse After FP32 Convergence
Apr 16, 2026Marcus ArmstrongLLM Quantization 4-Bit Quantization
Department of Computer Science University of Houston Houston, TX 77004
Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
Apr 15, 2026Jaemin Kim, Sungkyun Kim, Junyeol Lee +1LLM Quantization Language Model Calibration
Seoul National University · Hanyang University
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
Dissecting Quantization Error: A Concentration-Alignment Perspective
Mar 4, 2026Marco Federici, Boris van Breugel, Paul Whatmough +1LLM Quantization 4-Bit Quantization
Qualcomm AI Research
QTALE: Quantization-Robust Token-Adaptive Layer Execution for LLMs
Feb 11, 2026Kanghyun Noh, Jinheon Choi, Yulhwa KimLLM Quantization LLM Compression
Department of Semiconductor Convergence Engineering, Sungkyunkwan University · Department of Electrical and Computer Engineering, Sungkyunkwan University · Department of Semiconductor Systems Engineering, Sungkyunkwan University
Investigating Social Bias Changes in Quantized Language Models
Feb 5, 2026Stanley Z. Hua, Sanae Lotfi, Irene Y. ChenLLM Quantization Social Bias in Language Models
UC Berkeley & UCSF · Meta Superintelligence Labs
MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models
Feb 3, 2026Maximilian Kleinegger, Elvir Crnčević, Dan AlistarhLLM Quantization LLM Inference Acceleration
1Vienna University of Technology, Vienna, Austria · Institute of Science & Technology Austria (ISTA), Vienna, Austria · 3Red Hat AI, Boston, USA
Preserving Fairness and Safety in Quantized LLMs Through Critical Weight Protection
Jan 17, 2026Muhammad Alif Al Hakim, Alfan Farizki Wicaksono, Fajri KotoLLM Quantization Multilingual Language Model Evaluation
Faculty of Computer Science, Universitas Indonesia · Mohamed bin Zayed University of Artificial Intelligence
ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs
Jan 12, 2026Haoqian Meng, Yilun Luo, Yafei Zhao +3LLM Quantization 4-Bit Quantization
School of Computer Science and Technology, Tianjin University
When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models
Dec 22, 2025Michael S. Zhang, Rishi A. Ruia, Arnav Kewalram +3LLM Quantization Continual Learning for LLMs
Algoverse
You Had One Job: Per-Task Quantization Using LLMs' Hidden Representations
Nov 9, 2025Amit LeVi, Raz Lapid, Rom Himelstein +3Mixed-Precision Quantization LLM Quantization
1Technion—Israel Institute of Technology · 2Intuit · 3Ben-Gurion University of the Negev +1
LUQ: Layerwise Ultra-Low Bit Quantization for Multimodal Large Language Models
Sep 28, 2025Shubhang Bhatnagar, Andy Xu, Kar-Han Tan +1Mixed-Precision Quantization LLM Quantization
University of Illinois Urbana-Champaign · Work done as an intern at HP Inc. · University of California, Los Angeles +1
On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
Sep 22, 2025Rongguang Ye, Ming Tang, Edith C. H. NgaiMixed-Precision Quantization LLM Quantization
Southern University of Science and Technology · The University of Hong Kong
Fair-GPTQ: Bias-Aware Quantization for Large Language Models
Sep 18, 2025Irina Proskurina, Guillaume Metzler, Julien VelcinLLM Quantization Social Bias in Language Models
Université Claude Bernard Lyon 1, Université Lumière Lyon 2, ERIC · École Centrale de Lyon, LIRIS, CNRS UMR 5205
Squeeze10-LLM: Squeezing LLMs' Weights by 10 Times via a Staged Mixed-Precision Quantization Method
Jul 24, 2025Qingcheng Zhu, Yangyang Ren, Linlin Yang +6Mixed-Precision Quantization LLM Quantization
Beihang University · Communication University of China · Beijing Jiaotong University
FPTQuant: Function-Preserving Transforms for LLM Quantization
Jun 5, 2025Boris van Breugel, Yelysei Bondarenko, Paul Whatmough +1LLM Quantization LLM Inference Acceleration
Qualcomm AI Research, Amsterdam, Netherlands

May 17, 2026, cs.LGJ/K move · Enter open · S save

WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points

Dongyue Li, Zechun Liu, Kai Yi, Zhenshuo Zhang+5

1Northeastern University, MA · 2Meta AI, CA.

LLM Quantization Quantization-Aware Training Neural Network Optimization Low-Bit Quantization

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Quantization-aware training (QAT) is widely adopted to quantize language models by training full-precision weights using gradients from the quantized model. The main bottleneck is its slow convergence and early performance plateau, particularly below 4-bit-widths. While this problem has been observed in prior work, its precise cause remains unclear. In this paper, we analyze the convergence of QAT by estimating the spectrum of the loss-surface Hessians. We find that the weights converge to flat regions around saddle points, where a large fraction of the Hessian eigenvalues are both positive and negative. During training, an increasing fraction of Hessian eigenvalues concentrates around zero, whose magnitude decreases. At lower bit-widths, the magnitude of eigenvalues in the Hessian spectrum is significantly smaller. To mitigate these issues, we propose an algorithm called WinQ to accelerate QAT, which involves: (1) periodically resetting weights to the linear interpolation of full-precision and quantized weights, reducing the distance to the quantization grid and increasing eigenvalue magnitude, and (2) computing gradients of noise-injected weights to regularize the Hessian. Extensive experiments show that WinQ accelerates QAT by up to 4 times across various quantization methods and models. Under the same training cost, WinQ improves state-of-the-art sub-4-bit quantization by up to 8.8%. These results are consistent across 16 settings with different language models, quantization methods, and bit widths.

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Latest papers 246