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arXiv · 2510.16074

Early-stopping for Transformer model training

Jing He·Hua Jiang·Cheng Li·Siqian Xin·Shuzhen Yang
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Abstract

This work, based on Random Matrix Theory (RMT), introduces a novel early-stopping strategy for Transformer training dynamics. Utilizing the Power Law (PL) fit to tansformer attention matrices as a probe, we demarcate training into three stages: structural exploration, heavy-tailed structure stabilization, and convergence saturation. Empirically, we observe that the spectral density of the shallow self-attention matrix $V$ consistently evolves into a heavy-tailed distribution. Crucially, we propose two consistent and validation-set-free criteria: a quantitative metric for heavy-tailed dynamics and a novel spectral signature indicative of convergence. The strong alignment between these criteria highlights the utility of RMT for monitoring and diagnosing the progression of Transformer model training.

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Jing He, Hua Jiang, Cheng Li, Siqian Xin, Shuzhen Yang. 2025-10-17. Early-stopping for Transformer model training. https://arxiv.org/abs/2510.16074

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