Unstable Features, Reproducible Subspaces: Understanding Seed Dependence in Sparse Autoencoders

Gleb Gerasimov, Timofei Rusalev, Nikita Balagansky +3

Sparse autoencoders (SAEs) are widely used to interpret neural network representations, but their utility depends on whether the learned features are reproducible across training r…