Abstract: Variance reduction has emerged in recent years as a strong competitor to stochastic gradient descent in non-convex problems, providing the first algorithms to improve upon the converge rate of stochastic gradient descent for finding first-order critical points. However, variance reduction techniques typically require carefully tuned learning rates and willingness to use excessively large "mega-batches" in order to achieve their improved results. We present a new algorithm, STORM, that does not require any batches and makes use of adaptive learning rates, enabling simpler implementation and less hyperparameter tuning. Our technique for removing the batches uses a variant of momentum to achieve variance reduction in non-convex optimization. On smooth losses $F$, STORM finds a point $x$ with $\mathbb{E}[\Vert \mathrm{\nabla }F(x)\Vert ]\le O(1/\sqrt{T}+{\sigma }^{1/3}/T^{1/3})$ in $T$ iterations with ${\sigma }^2$ variance in the gradients, matching the best-known rate but without requiring knowledge of $\sigma$.