On Implications of Scaling Laws on Feature Superposition

Pavan Katta

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arXiv · 2407.01459

On Implications of Scaling Laws on Feature Superposition

Pavan Katta

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Abstract

Using results from scaling laws, this theoretical note argues that the following two statements cannot be simultaneously true: 1. Superposition hypothesis where sparse features are linearly represented across a layer is a complete theory of feature representation. 2. Features are universal, meaning two models trained on the same data and achieving equal performance will learn identical features.

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Pavan Katta. 2024-07-01. On Implications of Scaling Laws on Feature Superposition. https://arxiv.org/abs/2407.01459

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