eh2022-sae-top-k-sparsity-requirement
IN premise — summaries/2026/08/24/elhage-2022-toy-models-superposition-chunk-1.md
Created 2026-08-25T02:57:58+00:00
A Sparse Autoencoder (SAE) uses top-k sparsity constraint; without it, the autoencoder becomes a dense linear projection and loses interpretability
Summary
The top-k sparsity constraint is what forces each input to activate only a small, fixed set of features rather than blending across all of them. Without that hard limit, the autoencoder degenerates into an ordinary linear transform, and you lose the discrete, human-readable features that make the whole technique useful for understanding what a model is actually computing.