Complexity-Guided Component-wise Initialization for Language Model Pretraining
Quick Answer
The study explores the potential of reusing structured weight spectra from pretrained GPT-2 models for initialization in language model pretraining.
Quick Take
The study explores the potential of reusing structured weight spectra from pretrained GPT-2 models for initialization in language model pretraining. Despite observable changes in spectral patterns with new initialization schemes, performance gains were not achieved, indicating that pretrained weights remain the more effective choice for model training.
Key Points
- Analyzed eleven GPT-2-style checkpoints for spectral patterns and effective-rank entropy.
- Initialization schemes mimicked pretrained model magnitudes but did not improve performance.
- Pretrained-weight reuse remains competitive against new initialization methods.
- Spectral matching alone is insufficient for effective model optimization.
- Findings suggest pretrained spectra can diagnose model structure.
Paper Resources
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~2 min readAbstract: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)
— Originally published at arxiv.org
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