Methods · Sparse Autoencoders / SAE Variants and Architectural Improvements

JumpReLU SAEs

Survey sections: §6.3 SAE Variants and Architectural Improvements

JumpReLU SAEs (Rajamanoharan et al., 2024b) learn per-feature activation thresholds.

Assumptions and requirements

Straight-through estimators (Bengio et al., 2013) are used during training to enable gradient flow.

Intended applications

For models between 1B and 10B parameters, JumpReLU SAEs and Top-k SAEs are the most commonly reported variants; Gemma Scope (Lieberum et al., 2024) uses the former and Llama Scope (He et al., 2024) the latter.

Limitations

When extensive tuning is plausible, JumpReLU SAEs (Rajamanoharan et al., 2024b) achieved the strongest reconstruction performance at matched sparsity by learning per-feature thresholds. However, gains on these sparsity–fidelity proxy metrics do not reliably translate into better downstream performance (Karvonen et al., 2025). In practice, selecting the best model and hyperparameters to achieve optimal performance can take a lot of time.

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Key papers

Cited in §6.3.

Also discussed in: §6.4 Empirical Comparisons of SAE Variants · §6.4 Layer Type · §6.4 Resource Constraints · §6.4 Model Scale · §6.5.6 Practical Recommendations · §6.6.2 Cross-Layer Transcoders: Enabling Better Circuit Analysis

Other methods in Sparse Autoencoders / SAE Variants and Architectural Improvements

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