3. Once initialized, the behavioral search policy is as follows. In order to efficiently prune and expand the active determinant space s, we generate two ranked lists: the first list S 1 contains candidate determinants within the current space to be removed (given by low value in w) and the second list S 2 contains candidate determinants outside the current space s to be added (given by high value in Eq. ( )). The dimension of S 1 is the same as the selected subspace k, and nominally the dimension of S 2 is (N det -k), but for efficiency considerations, S 2 may be limited to the most important external determinants. In this work we make the top 150 determinants available for consideration in the search policy. Note that if an action is selected, the loop over S 1 is immediately terminated. Action pairs a = (p, q), p ∈ s, q / ∈ s from the two lists are iterated over, and an action a is taken if it satisfies a Metropolis-like criterion as detailed in Ref 43. If no action is selected, the learning procedure terminates.