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arXiv cs.CL
arXiv cs.CL
7/13/2026
Complexity-Guided Component-wise Initialization for Language Model Pretraining

Complexity-Guided Component-wise Initialization for Language Model Pretraining

Short summary

This paper investigates whether recurring spectral patterns in pretrained language model weights can serve as initialization signals for GPT-2-style pretraining. Analysis of eleven checkpoints reveals shared depth trends in spectral concentration, particularly in residual-writing matrices. However, while constructed initializers mimicking pretrained spectral profiles visibly change model structure, they do not yield performance advantages, suggesting that effective weight reuse requires richer information than component-wise scale and singular-value shape alone.

  • •Analyzes spectral patterns across 11 GPT-2-style checkpoints to find shared structural trends
  • •Constructs initialization schemes mimicking pretrained spectral profiles but finds no performance gain
  • •Concludes pretrained spectra are useful diagnostics but coarse spectral matching is insufficient for optimization

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