Apple Machine Learning Research reported a large-scale empirical study of Group Relative Policy Optimization (GRPO) in non-English and multilingual scenarios. The work covers different base models, training languages, and variants of rewards for the reasoning language.
According to the study synopsis, training reasoning in the native language often leaves only a small gap compared to training reasoning in English. The authors link the work to the approach of Reinforcement Learning on Verifiable Rewards (RLVR), which is often optimized using GRPO.
The practical significance of the finding is currently limited by the available description: the source does not provide gap sizes, a list of languages, models, or experimental conditions in the provided material. Therefore, the claim requires verification against the full research text and independent works.
editorial commentary
Why it matters
Probable implication: Developers of multilingual models may find it easier to consider the native reasoning language as a viable option, not just English. The next observable signals will be the full research text and independent results for specific languages. Significant uncertainty stems from the absence of numerical metrics, a list of languages, and experiment details in the provided synopsis.