The field of machine learning has developed a wide array of techniques for improving the effectiveness of performance elements. Ideally, a learning system would adapt its commitments to the demands of a particular learning situation, rather than relying on fixed commitments that impose tradeoffs between the efficiency and utility of a learning technique. This article presents an extension of the COMPOSER learning approach that dynamically adjusts its learning behavior based on the resources available for learning. COMPOSER is a speed-up learning technique that provides a statistical approach to the utility problem. The system identifies a sequence of transformations that, with high probability, increase the Type I utility of an initial planning system. The approach breaks the task into a learning phase and a utilization phase. This extension to COMPOSER adopts a rational policy that dynamically balances the trade-off between efficiency and utility. Implications for learning systems are discussed. (Contains 24 references.) (SLD)