Methods · Sparse Autoencoders / SAE Variants and Architectural Improvements

Top-k SAEs

Survey sections: §6.3 SAE Variants and Architectural Improvements

Top-k SAEs (Gao et al., 2025) impose an explicit hard sparsity constraint.

Assumptions and requirements

This formulation explicitly enforces ∥z∥0≤k\|z\|_0 \le k—a more direct feature-sparsity constraint than L1L_1 regularisation.

Intended applications

Under limited compute, or where fast iteration matters more than reconstruction quality, Top-k SAEs (Gao et al., 2025) are the practical choice: fixing ∥z∥0≤k\|z\|_0 \le k removes the sparsity coefficient λ\lambda from the search space entirely rather than requiring it to be tuned, with reported values commonly in the range k = 32 (Gao et al., 2025) to 150 (Karvonen et al., 2025).

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Key papers

Cited in §6.3.

Also discussed in: §6.4 Empirical Comparisons of SAE Variants · §6.4 Resource Constraints · §6.4 Model Scale · §6.5.6 Practical Recommendations

Other methods in Sparse Autoencoders / SAE Variants and Architectural Improvements

Sparse Autoencoders (SAEs) · Gated SAEs · JumpReLU SAEs · Batch Top-k SAEs