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Abstract:We analyze the dynamics of finite width effects in wide but finite feature learning neural networks. Unlike many prior analyses, our results, while perturbative in width, are non-perturbative in the strength of feature learning. Starting from a dynamical mean field theory (DMFT) description of infinite width deep neural network kernel and prediction dynamics, we provide a characterization of the $\mathcal{O}(1/\sqrt{\text{width}})$ fluctuations of the DMFT order parameters over random initialization of the network weights. In the lazy limit of network training, all kernels are random but static in time and the prediction variance has a universal form. However, in the rich, feature learning regime, the fluctuations of the kernels and predictions are dynamically coupled with variance that can be computed self-consistently. In two layer networks, we show how feature learning can dynamically reduce the variance of the final NTK and final network predictions. We also show how initialization variance can slow down online learning in wide but finite networks. In deeper networks, kernel variance can dramatically accumulate through subsequent layers at large feature learning strengths, but feature learning continues to improve the SNR of the feature kernels. In discrete time, we demonstrate that large learning rate phenomena such as edge of stability effects can be well captured by infinite width dynamics and that initialization variance can decrease dynamically. For CNNs trained on CIFAR-10, we empirically find significant corrections to both the bias and variance of network dynamics due to finite width.