Hugging Face Trending Papers October 22, 2026

SQuAT: Spherical Lattice Vector Quantization-Aware Training

Read the original on Hugging Face Trending Papers →

SQuAT introduces a shape‑gain vector‑quantization‑aware training (VQ‑QAT) method that separates weight direction and magnitude, using an $E_8$‑derived spherical codebook for directions and low‑bit scalar quantizers for gains. The approach employs distinct straight‑through estimators for shape and gain, enabling tangent‑space updates that act as implicit spherical optimization. Distributed exact nearest‑neighbor assignment reduces training time by 12.8×, allowing scaling to 10 B tokens and yielding state‑of‑the‑art accuracy on several large language models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv Machine Learning

Oct 23

SQuAT: Spherical Lattice Vector Quantization-Aware Training

SQuAT introduces a shape‑gain vector quantization‑aware training (VQ‑QAT) method that separates weight direction and magnitude, using an $E_8$‑derived spherical codebook for directions and low‑bit scalar quantizers for gains. By applying distinct straight‑through estimators (STE) to each component, SQuAT induces tangent‑space updates that act as an implicit spherical (Riemannian) optimization, improving training dynamics over standard STE approaches. A distributed exact nearest‑neighbor assignment scheme further reduces training time by 12.8×, enabling scaling to 10 B tokens and achieving state‑of‑the‑art accuracy on several large language models.

By Sebastian Loeschcke, Ivan Koryakovskiy, Tycho F. A. van der Ouderaa, Riccardo Del Chiaro, Mart van Baalen, Paul N. Whatmough, Markus Nagel

llms efficiency benchmarks

arXiv Machine Learning

Jul 8

Leech Lattice Vector Quantization for Efficient LLM Compression

arXiv:2603. 11021v2 Announce Type: replace Abstract: Scalar quantization of large language models (LLMs) is fundamentally limited by information-theoretic bounds.

By Tycho F. A. van der Ouderaa, Mart van Baalen, Paul Whatmough, Markus Nagel

llms efficiency benchmarks

arXiv Machine Learning

Jul 2

GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache

arXiv:2607. 01065v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) with extended context windows is increasingly constrained by the linear growth of Key-Value (KV) cache memory.

By Soosung Kim, Minjae Park, Eui-Young Chung, Jaeyong Chung

llms efficiency safety

arXiv AI

Oct 21

E8-QAT: Efficient 2-Bit Quantization-Aware Training with Implicit Lattice Codebooks

arXiv:2610.27699v1 Announce Type: new Abstract: Low-bit quantization reduces the memory footprint of large language models, but preserving accuracy with efficient training remains challenging. Scalar...

By Xinyu Lei, Hong Huang, Jiebin Zhang, Hongyu Li, Jinlong Song, Decheng Wu, Guanghua Yu, Jianchen Zhu, Sujian Li

llms efficiency benchmarks

arXiv Machine Learning

Sep 1

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

arXiv:2605.11222v2 Announce Type: replace Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (...

By Ryan Lucas, Mehdi Makni, Xiang Meng, Adam Deng, Rahul Mazumder

llms efficiency

arXiv Machine Learning

Oct 8

CurveTQ: Rotation-Free Trellis Quantization of LLM Weights via Curvature-Weighted Search

arXiv:2610.09212v1 Announce Type: new Abstract: The best two-bit weight quantizers for large language models, such as QTIP and Proteus, rotate each weight matrix by a random orthogonal transform, whi...

By Guanhua Ding, Zi Wang, Ruichao Li, Jack Liu

llms efficiency

One email a morning, machine-written

Related stories

SQuAT: Spherical Lattice Vector Quantization-Aware Training

Leech Lattice Vector Quantization for Efficient LLM Compression

GSRQ: Gain-Shape Residual Quantization for Sub-1-bit KV Cache

E8-QAT: Efficient 2-Bit Quantization-Aware Training with Implicit Lattice Codebooks

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

CurveTQ: Rotation-Free Trellis Quantization of LLM Weights via Curvature-Weighted Search