Researchers investigated whether recurring spectral patterns in pretrained GPT-2-style models can serve as an initialization signal for new pretraining runs. The study analyzed eleven checkpoints to measure Frobenius norm and effective-rank entropy across layers and Transformer subcomponents.
- Checkpoints exhibited shared depth trends, including increasing scale and stronger spectral concentration in residual-writing matrices.
- Initialization schemes were constructed to imitate the component-wise magnitudes and spectral profiles of pretrained models.
- Evaluation results showed no performance advantage from these initializers compared to standard weight initialization methods.
- Pretrained-weight reuse remained competitive, while coarse spectral matching proved unreliable as an optimization strategy.
The findings suggest that while pretrained spectra are useful diagnostics of model structure, effective reuse requires preserving richer information than just component-wise scale and singular-value shape.