Language models tend to excel in languages they see the most during (pre)training—leaving low-resource languages at a stark disadvantage. But what if we could boost performance without throwing (much) more data or compute at the problem? In this talk, I’ll present a set of resource-lean (read: “cheap”) strategies that enhance multilingual language understanding and generation in low-resource settings. I’ll show how conceptually effective knowledge transfer techniques—not just bigger models—can improve multilingual capabilities across three major fronts: (1) standard text-based LLMs, (2) vision-language models, and (3) code language models. The takeaway? Scaling isn’t the only answer: for truly inclusive multilingual language technology, we need stronger inductive biases and more conceptual innovation.