arXiv AI By Ziyi Wang, Nan Jiang, Guang Lin, Qifan Song

SQS: Bayesian DNN Compression through Sparse Quantized Sub-distributions

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arXiv Machine Learning
1d ago

Trainable Vector Quantization for Large Language Models

The paper introduces Trainable Vector Quantization (TVQ), a method that uses straight-through estimation to allow gradient descent optimization of vector-quantized large language models. TVQ reformulates VQ with trainable latent weights, enabling arbitrary objective minimization and is applied in a block-wise knowledge distillation framework to balance quality and training efficiency. Experiments show TVQ achieves state‑of‑the‑art low‑bit post‑training quantization, achieving near‑lossless 2‑bit quantization for LLaMA2‑70B with only a 1% accuracy drop on question‑answering benchmarks.

By Anastasiia Prutianova, Alina Bogdanova, Mikhail Shutov, Dmitrii Poimanov, Anatoly Gorkov, Ilia Zharikov
arXiv AI
Oct 9

When Lower Reconstruction Loss Hurts: Distributionally Robust Refinement for Low-Bit LLM Quantization

The paper examines weight-only post‑training quantization (PTQ) and finds that minimizing reconstruction loss does not always improve model performance, sometimes even harming it on calibration data. It shows that weights with low reconstruction loss on one distribution can perform worse when activation distributions shift. To address this, the authors introduce Distributionally Robust Quantization (DRQ), a refinement step that minimizes worst‑case reconstruction loss over a constrained set of activation distributions while keeping the quantization grid unchanged. Experiments demonstrate that DRQ improves models quantized by several PTQ methods across dense and mixture‑of‑experts large language models, offering better downstream performance without extra inference cost.

By Yanlong Zhao, Xiaoyuan Cheng, Huihang Liu, Baihua He, Xinyu Zhang, Harrison Bo Hua Zhu, Wenlong Chen, Li Zeng, Zhuo Sun