A practical guide to sparse autoencoders (SAEs) - Goodfire
What are sparse autoencoders (SAEs) useful for in practice? A practical guide to what SAEs are, when to use alternatives like probes, and how to train a good SAE for your own model.
If you’ve heard of interpretability, you’ve probably heard of sparse autoencoders (SAEs). Maybe you’ve read about Golden Gate Claude, or even the discovery of a new class of Alzheimer’s biomarkers, both of which used SAEs. To many casual observers, SAEs can seem synonymous with interpretability—but they’re actually used less often, and for different purposes, than you might expect. So what are sparse autoencoders useful for in practice? What are the alternatives, and when should you use them? How can you train a good SAE? This post aims to answer these questions. Contents What are SAEs?…
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