  We develop a new stochastic algorithm with variance reduction for solving
pseudo-monotone stochastic variational inequalities. Our method builds on
Tseng's forward-backward-forward (FBF) algorithm, which is known in the
deterministic literature to be a valuable alternative to Korpelevich's
extragradient method when solving variational inequalities over a convex and
closed set governed by pseudo-monotone, Lipschitz continuous operators. The
main computational advantage of Tseng's algorithm is that it relies only on a
single projection step and two independent queries of a stochastic oracle. Our
algorithm incorporates a variance reduction mechanism and leads to almost sure
(a.s.) convergence to an optimal solution. To the best of our knowledge, this
is the first stochastic look-ahead algorithm achieving this by using only a
single projection at each iteration..
