Equation 6 · What Interpretability Actually Costs to Do at Scale
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subscript
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and the specific variant OpenAI’s interpretability team used to push this to frontier scale, the TopK autoencoder, replaces the soft penalty with an explicit constraint: exactly k latents fire, chosen by magnitude, and the rest are hard-zeroed. Gao and colleagues introduced this variant, established scaling laws relating autoencoder size and sparsity to reconstruction error, and — this is the operative fact for a cost accounting — trained a sixteen-million-latent autoencoder on GPT-4’s activations over forty billion tokens [ 1 ] .
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