Learning from past experience allows a problem solver to increase its solvability horizon from simple to complex problems. For planners, learning involves a training phase during which knowledge is extracted from simple problems. But how are these simple problems constructed? All current learning and problem solving systems require the user to provide the training set. However it is rarely easy to identify problems that are both simple and useful for learning, especially in complex applications. In this paper, we present our initial research towards the automated or semiautomated identification of these simple problems. From a difficult problem and a corresponding partially completed search episode, we extract auxiliary problems with which to train the learner. We motivate this overlooked issue, describe our approach, and illustrate it with examples.