I Found a Hidden Ratio in Transformers That Predicts Geometric Stability [R]

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Summary

The article presents a discovered spectral ratio between MLP and attention norms that predicts geometric stability in transformer models, with an optimal range of 0.5–2 to prevent rank collapse.

I have analyzed some decoder transformer models using Lyapunov spectral analysis and found that the ratio of the MLP and attention spectral norms strongly indicates whether a model will eventually collapse to rank-1 or not by the final layers. I found that the spectral ratio is best kept around 0.5–2 for keeping the model stable till the final layers. Github repo: [https://github.com/yousef-rafat/the-1-1-rule](https://github.com/yousef-rafat/the-1-1-rule)
Original Article

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