Author

Baihua He

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#artificial intelligence Preprint Oct 2026

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

Weight-only post-training quantization (PTQ) relies heavily on reconstruction loss minimization to preserve model quality at low precision. We show that the weights favored by minimizing this loss need not yield better model performance on new tasks. In fact, we find that lower reconstruction loss can even degrade mode...

Yan-Long Zhao, Xiao-Yuan Cheng, Hui-Hang Liu et al. · 0 citations

Preprint Jul 2026

Optimal Mixture-of-Experts Model Averaging for Conditional Generative Models

Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a wide range of scientific and applied domains. As these models proliferate, practitioners often face multiple plausible generators whose performance can vary with the tas...

Shijin Gong, Baihua He, Xinyu Zhang · 0 citations

#machine learning Preprint Aug 2026

SR-OPSD: Self-Referenced On-Policy Self-Distillation

Extensive experiments across scientific evaluation, mathematical reasoning, and coding generation tasks with multiple large language models show that SR-OPSD achieves the state-of-the-art or competitive performance across various settings.

Zhuo Sun, Entong Li, Yan-Long Zhao et al. · 0 citations