Recent research has demonstrated that systematic mappings between phonological word forms and their meanings can facilitate language learning (e.g., in the form of sound symbolism or cues to grammatical categories). Yet, paradoxically from a learning viewpoint, most words have an arbitrary form-meaning mapping. We hypothesized that this paradox may reflect a division of labor between 2 different language learning functions: arbitrariness facilitates learning specific word meanings and systematicity facilitates learning to group words into categories. In a series of computational investigations and artificial language learning studies, we varied the extent to which the language was arbitrary or systematic. For both the simulations and the behavioral studies, we found that the optimal structure of the vocabulary for learning incorporated this division of labor. Corpus analyses of English and French indicate that these predicted patterns are also found in natural languages.