We often draw pragmatic inferences about a speaker's intentions from what they choose to say, but also from what they choose not to say in context. This suggests that pragmatic reasoning might allow modern neural network models to more efficiently learn on grounded language data from cooperative reference games. As a motivating case, consider an instance of the color reference task from<cite> Monroe et al. (2017)</cite> shown in the first row of Table 1 . In this task, a speaker communicates a target color to a listener in a context containing two distractor colors; the listener picks out the target based on what the speaker says. In the first instance from Table 1 , the speaker utters "dark blue" to describe the target. Whereas "dark" and "blue" also apply to the target, they lose their informativity in the presence of the distractors, and so the speaker pragmatically opts for "dark blue". A listener who is learning the language from such examples might draw several inferences from the speaker's utterance. First, under the assumption that the speaker is informative, a "literal" learner might infer that "dark blue" applies to the target shade more than the distractors. Second, a "pragmatic" learner might consider the cheaper alternatives-"dark" and "blue"-that have occurred in the presence of the same target in prior contexts, and infer that these alternative utterances must also apply to the distractors given the speaker's failure to use them. The pragmatic learner might thus gain more semantic knowledge from the same training instances than the literal learner: pragmatic reasoning can reduce the data complexity of learning.