Learning shows great promise to extend the generality and effectiveness of planning techniques. Research in this area has generated an impressive battery of techniques and a growing body of empirical successes. Unfortunately the formal properties of these systems are not well understood. This is highlighted by a growing corpus of demonstrations where learning actually degrades planning performance. In this paper we view learning to plan as a search problem. We argue that the complexity of this search precludes a general solution and can only be approached by making simplifying assumptions. We discuss the frequently unarticulated commitments which underly current learning approaches. From these we assemble a framework of simplifications which a learning planner can draw upon. These simplifications improve learning efficiency but not without tradeoffs.