Author

Rui-Kang Liu

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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

#artificial intelligence Preprint Aug 2026

REAL-Q: E2E LLM Quantization via Dynamic Gradient Descent

Real-Q (Real-time E2E-loss Aligned LLM Quantization), a novel PTQ paradigm that breaks this compromise: instead of diluting the objective for the sake of analytic tractability, REAL-Q targets an end-to-end-aligned surrogate of the global loss and refines it via fine-grained, dynamic Block-wise Gradient Descent applied...

Qian Zhang, Yao-Ming Li, Zheng Tan et al. · 0 citations