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cs.LG2026
Fast and Accurate Probing of In-Training LLMs' Downstream Performances
Zhichen Liu, Tianle Lun, Zhibin Wen +7
The paradigm of scaling Large Language Models (LLMs) in both parameter size and test time has pushed the boundaries of AI capabilities, but at the cost of making the traditional ge…
cs.LG2025
ROOT: Robust Orthogonalized Optimizer for Neural Network Training
Wei He, Kai Han, Hang Zhou +4
The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic imprecision and training instabi…