From Unverifiable Domain Text to Cross-Lingual RLVR via Code-Switched Task Synthesis
Reinforcement Learning with Verifiable Rewards (RLVR) can improve reasoning in large language models, but specialized non-English domains expose a data mismatch.
ProofPaper ↗
Key points
- 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 as a seed task format, we synthesize automatically verifiable tasks from domain passages and reshape them into cross-lingual variants.
- 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.
- These results suggest that code-switched training helps target-language inputs access domain reasoning learned more strongly in English.
Sources (1)
- [1]From Unverifiable Domain Text to Cross-Lingual RLVR via Code-Switched Task SynthesisarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 04:54 PM
Reinforcement Learning with Verifiable Rewards (RLVR) can improve reasoning in large language models, but specialized non-English domains expose a data mismatch.
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.
Extractive summary: sentences quoted from the sources.
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