CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning
While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by first introducing CUREMED-BENCH, a high-quality multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer, spanning thirteen languages, including underrepresented languages such as Amharic, Yoruba, and Swahili. Building on this dataset, we propose CURE-MED, a curriculum-informed reinforcement learning framework that integrates code-switching-aware supervised fine-tuning and Group Relative Policy Optimization to jointly improve logical correctness and language stability. Across thirteen languages, our approach consistently outperforms strong baselines and scales effectively, achieving 85.21% language consistency and 54.35% logical correctness at 7B parameters, and 94.96% language consistency and 70.04% logical correctness at 32B parameters. These results support reliable and equitable multilingual medical reasoning in LLMs. The code and dataset are available at https://cure-med.github.io/
69713325c1c7409747bf9491
Eric Onyame
claimed_verified
2026-01-22T08:45:41.805Z
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/avatars/649d978e56ebbed4370755f459d74fc3.svg
Eric Onyame
EricOnyame
user
EricOnyame
69713325c1c7409747bf9492
Akash Ghosh
claimed_verified
2026-01-26T08:33:38.145Z
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Akash Ghosh
Agcs12
user
Agcs12
69713325c1c7409747bf9493
Subhadip Baidya
69713325c1c7409747bf9494
Sriparna Saha
69713325c1c7409747bf9495
Xiuying Chen
69713325c1c7409747bf9496
Chirag Agarwal
CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning
While large language models (LLMs) have shown to perform well on monolingual mathematical and commonsense reasoning, they remain unreliable for multilingual medical reasoning applications, hindering their deployment in multilingual healthcare settings. We address this by first introducing CUREMED-BENCH, a high-quality multilingual medical reasoning dataset with open-ended reasoning queries with a single verifiable answer, spanning thirteen languages, including underrepresented languages such as Amharic, Yoruba, and Swahili. Building on this dataset, we propose CURE-MED, a curriculum-informed reinforcement learning framework that integrates code-switching-aware supervised fine-tuning and Group Relative Policy Optimization to jointly improve logical correctness and language stability. Across thirteen languages, our approach consistently outperforms strong baselines and scales effectively, achieving 85.21% language consistency and 54.35% logical correctness at 7B parameters, and 94.96% language consistency and 70.04% logical correctness at 32B parameters. These results support reliable and equitable multilingual medical reasoning in LLMs. The code and dataset are available at https://cure-med.github.io/
A multilingual medical reasoning framework using curriculum-informed reinforcement learning achieves high language consistency and logical correctness across thirteen languages including underrepresented ones.
large language models multilingual medical reasoning CUREMED-BENCH curriculum-informed reinforcement learning code-switching-aware supervised fine-tuning Group Relative Policy Optimization logical correctness language consistency