CURE-MED-1.5B is a 1.5 billion parameter large language model specialized for multilingual medical reasoning, fine-tuned from Qwen/Qwen1.5-1.5B-instruct using a curriculum-informed reinforcement learning framework to enhance logical correctness and language stability in healthcare applications.

CURE-MED-1.5B is part of the CURE-MED family of models, designed to address the challenges of multilingual medical reasoning in large language models (LLMs). Built on the Qwen2.5-1.5B-instruct model, it incorporates a curriculum-informed reinforcement learning approach that integrates code-switching-aware supervised fine-tuning (SFT) and Group Relative Policy Optimization (GRPO) to improve performance on open-ended medical queries across 13 languages, including underrepresented ones such as Amharic, Yoruba, and Swahili. The model is trained and evaluated using CUREMED-BENCH, a high-quality multilingual open-ended medical reasoning benchmark with single verifiable answers.

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