The ability to rapidly acquire knowledge from humans is a fundamental skill for AI assistants. Traditional frameworks like imitation and reinforcement learning employ fixed, low-level communication protocols, making them inefficientfor teaching complex tasks. In contrast, humans are capable of communicatingnuanced ideas with progressive efficiency by establishing shared vocabularieswith others and expanding those vocabularies with increasingly abstract words. Mimicking this phenomenon in human communication, we introduce a novel learning framework named Communication-Efficient Interactive Learning (CEIL).By equipping a learning agent with a rich, dynamic language and an intrinsic motivation to communicate with minimal effort, CEIL leads to emergence of a human-like pattern where the learner and the teacher communicate more efficientlyover time by exchanging increasingly more abstract intentions. CEIL demonstrates impressive learning efficiency on a 2D MineCraft domain featuring long-horizondecision-making tasks. Especially, it performs robustly with teachers modeled after human pragmatic communication behavior.