Systems, methods, apparatuses, and computer program products to receive a plurality of binary weight values for a binary neural network sampled from a policy neural network comprising a posterior distribution conditioned on a theta value. An error of a forward propagation of the binary neural network may be determined based on a training data and the received plurality of binary weight values. A respective gradient value may be computed for the plurality of binary weight values based on a backward propagation of the binary neural network. The theta value for the posterior distribution may be updated using reward values computed based on the gradient values, the plurality of binary weight values, and a scaling factor.