  Discrete optimization belongs to the set of $\mathcal{NP}$-hard problems,
spanning fields such as mixed-integer programming and combinatorial
optimization. A current standard approach to solving convex discrete
optimization problems is the use of cutting-plane algorithms, which reach
optimal solutions by iteratively adding inequalities known as \textit{cuts} to
refine a feasible set. Despite the existence of a number of general-purpose
cut-generating algorithms, large-scale discrete optimization problems continue
to suffer from intractability. In this work, we propose a method for
accelerating cutting-plane algorithms via reinforcement learning. Our approach
uses learned policies as surrogates for $\mathcal{NP}$-hard elements of the cut
generating procedure in a way that (i) accelerates convergence, and (ii)
retains guarantees of optimality. We apply our method on two types of problems
where cutting-plane algorithms are commonly used: stochastic optimization, and
mixed-integer quadratic programming. We observe the benefits of our method when
applied to Benders decomposition (stochastic optimization) and iterative loss
approximation (quadratic programming), achieving up to $45\%$ faster average
convergence when compared to modern alternative algorithms.
