HiMed: Incentivizing Hindi Reasoning in Medical LLMs

Abstract:Medical large language models hold promise for reducing healthcare disparities, yet Hindi remains severely underrepresented. While medical LLMs excel in high-resource languages, their performance degrades sharply in Hindi, particularly on Indian systems of medicine. We argue that robust cross-lingual medical transfer requires Hindi reasoning. To this end, we introduce HiMed, a Hindi reasoning medical corpus and benchmark suite covering both Western and Indian medicine. We further propose HiMed-8B, a Hindi-form medical reasoning LLM, through the design of decaying scaffolding reward. Extensive experiments demonstrate improvement in Hindi medical reasoning performance and a reduction in the English-Hindi accuracy gap. Ablation studies validate the contribution of each training stage and reward component. All code, data, and weights are available

Computation and Language (cs.CL)

arXiv:2605.24635 [cs.CL]

(or arXiv:2605.24635v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.24635 Focus to learn more arXiv-issued DOI via DataCite

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