arXiv AI
Jun 4
Model-Preserving Adaptive Rounding
arXiv:2505. 22988v3 Announce Type: replace-cross Abstract: The goal of quantization is to produce a compressed model whose output distribution is as close to the original model's as possible.
BaKron: Efficient Quantization with Kronecker-Factored Hessians
arXiv:2608. 06291v1 Announce Type: cross Abstract: We accelerate a family of algorithms for neural network quantization whose geometry is informed by any Kronecker-factored approximation of the Hessian.
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arXiv Machine Learning
Jul 10
KronQ: LLM Quantization via Kronecker-Factored Hessian
arXiv:2607. 07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining.
arXiv Machine Learning
Jun 19
CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training
arXiv:2510. 18784v3 Announce Type: replace Abstract: Despite significant work on low-bit quantization-aware training (QAT), there is still an accuracy gap between such techniques and native training.
arXiv Machine Learning
Jul 30
GPTQ-2D: Cubic-Time Two-Sided Adaptive Rounding
arXiv:2607. 27042v1 Announce Type: cross Abstract: Adaptive rounding methods such as GPTQ, or equivalently Babai's nearest plane algorithm, round a real matrix to integers under a quadratic metric.
arXiv AI
Jun 18
HeRo-Q: A General Framework for Stable Low Bit Quantization via Hessian Conditioning
arXiv:2601. 21626v2 Announce Type: replace-cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error.
arXiv Machine Learning
Jul 22
Contraction-Gauge Preconditioning for Quantized Matrix Multiplication
arXiv:2607. 18745v1 Announce Type: new Abstract: We study low-precision computation of C=AB with both factors quantized.
arXiv Machine Learning
Aug 4
Simplified Quadratic Gradient: A Unified Framework Bridging Gradient Descent and Newton-Type Methods by Synthesizing Hessians and Gradients
arXiv:2209. 03282v5 Announce Type: replace-cross Abstract: Accelerating the convergence of second-order optimization, particularly Newton-type methods, remains a pivotal challenge in algorithmic research.
arXiv Machine Learning
Jun 2
Inner Product Aware Quantization: Provably Fast, Accurate, and Adaptive Algorithms
arXiv:2606. 00289v1 Announce Type: new Abstract: Quantization is a fundamental tool used to compress datasets, neural network weights, and memory usage in a range of computational tasks.
arXiv Machine Learning
Jul 24
KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers
arXiv:2607. 21446v1 Announce Type: new Abstract: Post-training quantization (PTQ) of diffusion transformers (DiTs) to W4A4 severely degrades output quality, because activations entering each linear layer contain outliers that 4-bit formats cannot represent.
arXiv Machine Learning
Jun 2
Rethinking Bregman Divergences in Kronecker-Factored Optimizers
arXiv:2606. 00542v1 Announce Type: new Abstract: Shampoo-style optimizers approximate gradient covariance matrices using Kronecker-factored structures.
arXiv Machine Learning
Jun 2
Quartet II: Accurate LLM Pre-Training in NVFP4 by Improved Unbiased Gradient Estimation
arXiv:2601. 22813v2 Announce Type: replace Abstract: The NVFP4 lower-precision format, supported in hardware by NVIDIA Blackwell GPUs, promises to allow, for the first time, end-to-end fully-quantized pre-training of massive models such as LLMs.
arXiv Machine Learning
Jul 24
MuonQ: Enhancing Low-Bit Muon Quantization via Directional Fidelity Optimization
arXiv:2605. 11396v2 Announce Type: replace Abstract: The Muon optimizer has emerged as a compelling alternative to Adam for training large language models, achieving remarkable computational savings through gradient orthogonalization.