Equation 1 · What Interpretability Actually Costs to Do at Scale
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Symbol d
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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
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