Soar is an architecture for a system that is intended to be capable of general intelligence. Chunking, a simpleexperience-based learning mechanism, is Soar’s only learning mechanism. Chunking creates new items ofinformation, called chunks, based on the results of problem-solving and stores them in the knowledge base. Thesechunks are accessed and used in appropriate later situations to avoid the problem-solving required to determinethem. It is already well-established that chunking improves performance in Soar when viewed in terms of thesubproblems required and the number of steps within a subproblem. However, despite the reduction in number ofsteps, sometimes there may be a severe degradation in the total run time. This problem arises due toexpensivechunks, i.e., chunks that require a large amount of effort in accessing them from the knowledge base. They pose amajor problem for Soar, since in their presence, no guarantees can be given about Soar’s performance.In this article, we establish that expensive chunks exist and analyze their causes. We use this analysis to propose asolution for expensive chunks. The solution is based on the notion of restricting the expressiveness of therepresentational language to guarantee that the chunks formed will require only a limited amount of accessing effort.We analyze the tradeoffs involved in restricting expressiveness and present some empirical evidence to support ouranalysis.