Scaling Laws for SAE Training Data

Sparse autoencoder (SAE) training is often bottlenecked by activation storage. We show that the stored activation buffer can be far smaller than the total SAE training budget: quality depends mainly on the number of \emph{unique} activations, not on whether every training token is fresh. After a short diversity-limited regime, additional fresh activations provide little benefit; replaying the same buffer preserves both reconstruction and interpretability performance. We capture this diversity--repetition tradeoff with a data-constrained scaling law and validate it across dictionary sizes, token budgets, Llama-3.1 and Qwen3 models, early/middle/late layers, BatchTopK/TopK/JumpReLU SAEs, and downstream metrics including EV, reconstruction MSE, feature absorption, automated interpretability, and sparse probing. The resulting buffer-sizing rule is simple: choose the smallest activation buffer that reaches the quality plateau, then use replay to spend the remaining training budget. In our experiments, this reduces activation storage by $8\text{--}64\times$ with negligible quality loss.