Complexity-Guided Component-wise Initialization for Language Model Pretraining
This study analyzes weight spectra of eleven GPT-2-style pretrained models, finding shared depth trends such as increasing scale and spectral concentration in residual-writing matrices. The authors construct initialization schemes that mimic these spectral patterns, but find no performance advantage over standard methods. Pretrained weight reuse remains competitive, suggesting that coarse spectral matching is insufficient for effective reuse; richer information is needed.
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[Submitted on 10 Jul 2026]
Title:Complexity-Guided Component-wise Initialization for Language Model Pretraining
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Abstract:Pretrained language models often exhibit structured weight spectra, suggesting that training may repeatedly produce similar layerwise and component-wise organization. We ask whether these recurring spectral patterns can be reused as an initialization signal for GPT-2-style language-model pretraining. First, we analyze eleven pretrained GPT-2-style checkpoints that vary in size, language, tokenizer, and training corpus, measuring Frobenius norm and effective-rank entropy across layers and Transformer subcomponents. The checkpoints show shared depth trends, especially increasing scale and stronger spectral concentration in residual-writing matrices. We then construct initialization schemes that imitate the component-wise magnitudes and spectral profiles of pretrained models, and compare them with several weight initialization methods. These initializers visibly change the model's structural spectral patterns, but the evaluation results do not show a corresponding performance advantage. Pretrained-weight reuse remains competitive, while coarse spectral matching alone is not a reliable optimization strategy. Our results suggest that pretrained spectra are useful diagnostics of trained model structure, but that effective reuse likely requires preserving richer information than component-wise scale and singular-value shape.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2607.09204 [cs.CL]
(or arXiv:2607.09204v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09204
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Konstantin Garbers [view email] [v1] Fri, 10 Jul 2026 08:49:39 UTC (199 KB)
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