  Unrolled computation graphs are prevalent throughout machine learning but
present challenges to automatic differentiation (AD) gradient estimation
methods when their loss functions exhibit extreme local sensitivtiy,
discontinuity, or blackbox characteristics. In such scenarios, online evolution
strategies methods are a more capable alternative, while being more
parallelizable than vanilla evolution strategies (ES) by interleaving partial
unrolls and gradient updates. In this work, we propose a general class of
unbiased online evolution strategies methods. We analytically and empirically
characterize the variance of this class of gradient estimators and identify the
one with the least variance, which we term Noise-Reuse Evolution Strategies
(NRES). Experimentally, we show NRES results in faster convergence than
existing AD and ES methods in terms of wall-clock time and number of unroll
steps across a variety of applications, including learning dynamical systems,
meta-training learned optimizers, and reinforcement learning.
