GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings
arXiv:2608.13698v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for improving the reasoning capabilities of pretrained language models but current studies remain heavily English-centric. We conduct a large-scale empirical study of multilingual and non-English GRPO across a wide range of base models, training languages, and different reasoning language rewards. We find that training to reason in the native language often leaves only a small gap to training for English reasoning. We further observe strong crosslingual transfer: training in one language often improves performance in many others. However, specific trends are highly model- and language-dependent. In some cases, training in a particular language induces severe regressions on out-of-domain capabilities in other languages. Our analysis shows that RLVR beyond English can provide broad crosslingual gains, but also requires broad evaluation to detect language-specific regressions.
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[Submitted on 13 Aug 2026]
Title:GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings
View a PDF of the paper titled GRPO Beyond English: A Large-Scale Study of GRPO in Non-English and Multilingual Settings, by Konstantin Dobler and 4 other authors
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Abstract:Reinforcement Learning with Verifiable Rewards (RLVR), often optimized with Group Relative Policy Optimization (GRPO), has become a central recipe for improving the reasoning capabilities of pretrained language models but current studies remain heavily English-centric. We conduct a large-scale empirical study of multilingual and non-English GRPO across a wide range of base models, training languages, and different reasoning language rewards. We find that training to reason in the native language often leaves only a small gap to training for English reasoning. We further observe strong crosslingual transfer: training in one language often improves performance in many others. However, specific trends are highly model- and language-dependent. In some cases, training in a particular language induces severe regressions on out-of-domain capabilities in other languages. Our analysis shows that RLVR beyond English can provide broad crosslingual gains, but also requires broad evaluation to detect language-specific regressions.
Subjects:
Computation and Language (cs.CL); Machine Learning (cs.LG)
Cite as: arXiv:2608.13698 [cs.CL]
(or arXiv:2608.13698v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.13698
arXiv-issued DOI via DataCite (pending registration)
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From: Konstantin Dobler [view email] [v1] Thu, 13 Aug 2026 18:43:33 UTC (14,393 KB)
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