Apple Research Shows Single Language Training Can Enhance Multilingual Abilities

Apple's research team conducted reinforcement learning experiments using 9 models and 11 languages, discovering that models trained in a single language can significantly improve problem-solving abilities in other languages. In a French test, models trained directly in French showed an average score increase of 25.6 percentage points, while models trained only in Spanish also improved by 24.6 percentage points in the French test, with a mere 1 percentage point difference. The models not only learn problems specific to a particular language but also master problem-solving methods that can be applied across languages. Nevertheless, the choice of training language must be made cautiously, as certain languages may lead to a decline in abilities. For instance, the Qwen3-4B model showed a 19.2 percentage point drop in English test scores after training in Swahili, while multilingual training improved scores by 4.5 percentage points. This study primarily validated cross-language abilities in reasoning tasks such as mathematics and logic, without involving language-dependent tasks like reading comprehension and cultural knowledge.

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