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    <abstract>Reinforcement Learning with Verifiable Rewards (RLVR) can improve reasoning in large language models, but specialized non-English domains expose a data mismatch. Raw English domain text is often available, while target-language RLVR tasks with verifiable rewards are scarce. We introduce code-switched task synthesis for cross-lingual RLVR, a data-centric approach that derives target-language and code-switched RLVR task variants from otherwise unverifiable English domain text. Using fill-in-the-middle multiple-choice QA (FIM-MCQA) as a seed task format, we synthesize automatically verifiable tasks from domain passages and reshape them into cross-lingual variants. These variants preserve the same evidence-answer relation but change how English and target-language evidence are presented during RLVR. On Korean medical benchmarks and cybersecurity benchmarks across four target languages, code-switched training outperforms matched monolingual and bilingual-mixture baselines on average target-language performance while maintaining comparable English performance. Additional analyses provide evidence of better target-language input understanding and stronger cross-lingual alignment in both model outputs and internal representations. These results suggest that code-switched training helps target-language inputs access domain reasoning learned more strongly in English.</abstract>
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%0 Conference Proceedings
%T From Unverifiable Domain Text to Cross-Lingual RLVR via Code-Switched Task Synthesis
%A Park, Cheonbok
%A Kim, Jihwan
%A Joo, Shinyoung
%A Lee, Joosung
%A Jo, Hwiyeol
%A Kim, Jeonghoon
%A Choo, Jaegul
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F park-etal-2026-unverifiable
%X Reinforcement Learning with Verifiable Rewards (RLVR) can improve reasoning in large language models, but specialized non-English domains expose a data mismatch. Raw English domain text is often available, while target-language RLVR tasks with verifiable rewards are scarce. We introduce code-switched task synthesis for cross-lingual RLVR, a data-centric approach that derives target-language and code-switched RLVR task variants from otherwise unverifiable English domain text. Using fill-in-the-middle multiple-choice QA (FIM-MCQA) as a seed task format, we synthesize automatically verifiable tasks from domain passages and reshape them into cross-lingual variants. These variants preserve the same evidence-answer relation but change how English and target-language evidence are presented during RLVR. On Korean medical benchmarks and cybersecurity benchmarks across four target languages, code-switched training outperforms matched monolingual and bilingual-mixture baselines on average target-language performance while maintaining comparable English performance. Additional analyses provide evidence of better target-language input understanding and stronger cross-lingual alignment in both model outputs and internal representations. These results suggest that code-switched training helps target-language inputs access domain reasoning learned more strongly in English.
%U https://aclanthology.org/2026.emnlp-main.1375/
%P 31382-31413

Markdown (Informal)

[From Unverifiable Domain Text to Cross-Lingual RLVR via Code-Switched Task Synthesis](https://aclanthology.org/2026.emnlp-main.1375/) (Park et al., EMNLP 2026)

• From Unverifiable Domain Text to Cross-Lingual RLVR via Code-Switched Task Synthesis (Park et al., EMNLP 2026)

ACL

• Cheonbok Park, Jihwan Kim, Shinyoung Joo, Joosung Lee, Hwiyeol Jo, Jeonghoon Kim, and Jaegul Choo. 2026. From Unverifiable Domain Text to Cross-Lingual RLVR via Code-Switched Task Synthesis. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 31382–31413, Budapest, Hungary. Association for Computational Linguistics.