Sometimes less is more: Investigating inductive biases of MLPs and Transformers on noisy k-parity features

arXiv:2610.37976v1 Announce Type: new Abstract: We empirically investigate how MLPs and Transformers differ when trained on data sets containing one or more features encoded by noisy k-parity. Noisy k-parity gives us hard synthetic data sets in which we can control the predictivity and availability of every feature. Across a range of data distributions, data scales and model sizes, we find large differences in the training dynamics and inductive biases of the two architectures: Transformers are more robust to noisy features but struggle to learn complex, low-availability features, whereas MLPs show the opposite trend and improve steadily with more data.