Author

Feiyu Wang

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#artificial intelligence Preprint Sep 2026

G$^2$PTQ: Improving LLM Post-Training Quantization with Generalized Gradient Compensation

G$^2$PTQ is presented, a unified PTQ framework with Generalized Gradient Compensation that integrates both first- and second-order information under a globally supervised, block-wise optimization objective and enables better alignment with the full-precision model, outperforming state-of-the-art baselines.

Rui-Kang Liu, Hao-Li Bai, Yuxuan Sun et al. · 0 citations

Preprint Aug 2026

ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization

ReQuant is introduced, a backpropagation-free fixed-grid refinement procedure that takes an existing quantized model as a feasible starting point and iteratively revisits its discrete weight assignments on the fixed quantization grid, and turns the initially fixed PTQ output into an iteratively optimizable discrete sol...

Yongge Ma, Guoan Wang, Feiyu Wang et al. · 0 citations