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A sparse autoencoder (SAE) is trained to reconstruct a model’s internal activation vectors through a sparse bottleneck: an encoder maps an activation of dimension d into a much wider space of n candidate “features,” a sparsity constraint keeps only k of those features active per token, and a decoder reconstructs the original activation from just those k . The training objective, in its standard form, is $\mathcal{L}(x) = \lVert x - \hat{x}(x) \rVert_2^2 + \lambda \lVert f(x) \rVert_1, \qquad \hat{x}(x) = W_d\, f(x) + b_d$. and the specific variant OpenAI’s interpretability team used to push this to frontier scale, the TopK autoencoder, replaces the soft $\ell_1$ penalty with an explicit constraint: exactly k latents fire, chosen by magnitude, and the rest are hard-zeroed. Gao and colleagues…

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