Author

Yan-Jie Fu

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#artificial intelligence Preprint Oct 2026

Which and When to Admit: Gradient Admission for Data-Centric Small Language Model Finetuning

LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to three structural problems: conflicting gradients that cancel, static data selection that cannot track evolving learning dynamics, and subspace saturation that causes later up...

Hong-Yu Cao, Yan-Chi Liu, Kun-Peng Liu et al. · 0 citations

#artificial intelligence Preprint Oct 2026

Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting

In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between training and deployment (covariate shift); 3) validation sets often diverge from unseen test environments; or 4) standard generative models simply mimic outdated source d...

Hong-Yu Cao, Xin-Yuan Wang, Arun Vignesh Malarkkan et al. · 1 citation