We systematically investigate cross-lingual ability transfer in language models through controlled experiments across three problem sets: algorithmic addition, graph navigation, and natural language modeling. Our experimental design creates high-resource and low-resource “language” pairs differing in vocabulary, grammar, and computational requirements. We show that training on mixed datasets consistently enables strong positive transfer, significantly improving low-resource language performance compared to training on low amount of data in isolation. We observe improvements from 0% to 100% accuracy in arithmetic tasks, from 24% to 98% accuracy in graph navigation tasks, and 69.6% perplexity reduction in natural language modeling. We demonstrate that transfer effectiveness depends on computational complexity and linguistic differences, where grammar modifications support stronger transfer than vocabulary modifications. These findings provide compelling evidence that cross-lingual ability transfer is a robust mechanism which contributes to the quality of large language models in low-resource languages.