Forward gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements.The cost is the requirement to guess the step direction, which is hard in high dimensions.While current solutions rely on weighted averages over isotropic guess vector distributions,we propose to stro…