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Summary: The paper introduces an algorithm (MORE) for black box optimization that constructs local quadratic surrogate'' models based on recent observations. By using a (simpler than the true function) quadratic model, the iterative step of refining the reward parameters can be computed in closed form (though the models themselves seem to be built with a form of sampling). This approach allows for a more sample efficient and robust search procedure, which is shown to outperform state-of-the-art methods in terms of samples and converged parameters on a number of simple functions as well as some very complex robotics tasks. Review: The new algorithm performs much better than the state-of-the-art algorithms in a wide range of experiments and is applicable in a very important problem setting. I appreciate the wide range of problems that the authors used and I think they make a very strong case for the new algorithm.