We compare pragmatic and non-pragmatic models at training and at test, while varying conditions on the training data to test hypotheses regarding the utility of pragmatic inference for learning. In particular, we show that incorporating pragmatic reasoning at training time yields improved, state-of-the-art accuracy for listener models on the color reference task from<cite> Monroe et al. (2017)</cite> , and the effect demonstrated by this improvement is especially large under small training data sizes. We further introduce a new color-grid reference task and data set consisting of higher dimensional objects and more complex speaker language; we find that the effect of pragmatic listener training is even larger in this setting.