Reinforcement Learning with Verifiable Rewards (RLVR) has shown promise for improving reasoning in large language models, but it stumbles when applied to specialized non-English domains. According to a new arXiv preprint, the core issue is a data mismatch: raw English domain text is often plentiful, while the target-language data needed for verifiable reward tasks is not.
The paper proposes a synthesis approach that converts unverifiable English domain text into cross-lingual RLVR tasks via code-switching. By mixing English source content with the target language, the method aims to generate tasks that carry verifiable rewards without requiring extensive native-language annotations.
The abstract does not include experimental results or comparisons, so the effectiveness of the approach remains to be seen. Still, the idea offers a practical direction for extending RLVR beyond English-centric reasoning benchmarks into specialized multilingual settings.