Elamur Research⌕
☰
home / papers / 2026 / CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning

Paper · 2026 · Eric Onyame, Akash Ghosh, Subhadip Baidya et al.

Summary

Large language models handle math and commonsense reasoning reasonably well in English, but they often fail at medical reasoning in other languages. Their answers lose accuracy, and the reasoning drifts between languages. To address this, the authors built CUREMED-BENCH, a set of 15,774 open-ended medical questions in 13 languages, each with one verifiable answer. The languages include low-resource ones such as Amharic, Yoruba, Hausa and Swahili. The questions are grounded in MedlinePlus content and were checked by native speakers and clinicians.

They also propose CURE-MED, a two-stage training scheme for Qwen2.5-Instruct models:

  • Stage 1: supervised fine-tuning on reasoning chains that permit controlled code-switching.
  • Stage 2: GRPO reinforcement learning ordered as a curriculum, moving from high- to mid- to low-resource languages. Rewards target both answer correctness and staying in the target language.

CURE-MED beats strong baselines at every model size and generalizes to unseen benchmarks. This matters for fair and trustworthy multilingual clinical AI, especially in languages underserved by current models.

Key points

  • Dataset construction: GPT-4o drafted multiple-choice items from MedlinePlus. Questions that Qwen2.5-3B/7B and LLaMA-3.1-8B all answered correctly were dropped as too easy. The remaining items were rewritten as open-ended questions with reasoning traces. Expert reviewers rated the result 4.89/5 on average.
  • Main results: The 7B model reaches 85.21% language consistency and 54.35% logical correctness. The 32B model reaches 94.96% and 70.04%.
  • Ablations: Plain multilingual SFT helps little; at 3B, logic accuracy actually fell from 10.83% to 9.50%. Code-switched SFT gives large gains; at 1.5B, language consistency rose from 3.84% to 53.67%. Curriculum GRPO adds further improvement over naïve single-stage GRPO.
  • Out-of-distribution performance: CURE-MED-32B tops MMedBench at 79.57%. It stays competitive on MedQA and MedExpQA, where HuatuoGPT-70B is slightly ahead.
  • Limitations: The work depends on proprietary APIs. The benchmark does not cover multi-visit care or multimodal clinical evidence.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of VirginiaCharlottesville · United States36 works · 9,730 citations🎓Indian Institute of Technology PatnaPatna · India21 works · 374 citations🎓Indian Institute of Technology KanpurKanpur · India14 works · 619 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksComputer Vision and Pattern Recognition · 2,869 worksMedicine · 241 worksHealth Informatics · 184 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 1,426 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as Eric Onyame, Akash Ghosh, Subhadip Baidya, Sriparna Saha, Xiuying Chen and Chirag Agarwal. 2026. CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1416

@inproceedings{onyame2026cure,
  title = {{CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning}},
  author = {Eric Onyame, Akash Ghosh, Subhadip Baidya, Sriparna Saha, Xiuying Chen and Chirag Agarwal},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1416},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1416}
}

References

72 references, 32 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, and 1 others. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774.
  2. Iñigo Alonso, Maite Oronoz, and Rodrigo Agerri. 2024. Medexpqa: Multilingual benchmarking of large language models for medical question answering. Artificial intelligence in medicine, 155:102938. find
  3. Julia Amann, Alessandro Blasimme, Effy Vayena, Dietmar Frey, Vince I Madai, and Precise4Q Consortium. 2020. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC medical informatics and decision making, 20(1):310. find
  4. Jose F Arocha, Dongwen Wang, and Vimla L Patel. 2005. Identifying reasoning strategies in medical decision making: a methodological guide. Journal of biomedical informatics, 38(2):154–171. find
  5. Hritik Bansal, John Dang, and Aditya Grover. 2023. Peering through preferences: Unraveling feedback acquisition for aligning large language models. arXiv preprint arXiv:2308.15812. — [arXiv:2308.15812](https://arxiv.org/abs/2308.15812) find
  6. Samuel Cahyawijaya, Holy Lovenia, and Pascale Fung. 2024. Llms are few-shot in-context low-resource language learners. arXiv preprint arXiv:2403.16512.
  7. Hanjie Chen, Zhouxiang Fang, Yash Singla, and Mark Dredze. 2025. Benchmarking large language models on answering and explaining challenging medical questions. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language…
  8. Junying Chen, Zhenyang Cai, Ke Ji, Xidong Wang, Wanlong Liu, Rongsheng Wang, Jianye Hou, and Benyou Wang. 2024. Huatuogpt-o1, towards medical complex reasoning with llms. arXiv preprint arXiv:2412.18925. find
  9. Nuo Chen, Zinan Zheng, Ning Wu, Ming Gong, Dongmei Zhang, and Jia Li. 2023a. Breaking language barriers in multilingual mathematical reasoning: Insights and observations. arXiv preprint arXiv:2310.20246. find
  10. Zeming Chen, Alejandro Hernández Cano, Angelika Romanou, Antoine Bonnet, Kyle Matoba, Francesco Salvi, Matteo Pagliardini, Simin Fan, Andreas Köpf, Amirkeivan Mohtashami, and 1 others. 2023b. Meditron-70b: Scaling medical pretraining for large language models. arXiv preprint arXiv:2311.16079.
  11. Akash Ghosh, Debayan Datta, Sriparna Saha, and Chirag Agarwal. 2025a. The multilingual mind: A survey of multilingual reasoning in language models. arXiv preprint arXiv:2502.09457. find
  12. Akash Ghosh, Srivarshinee Sridhar, Raghav Kaushik Ravi, Muhsin Muhsin, Sriparna Saha, and Chirag Agarwal. 2025b. Clinic: Evaluating multilingual trustworthiness in language models for healthcare. arXiv preprint arXiv:2512.11437. — [arXiv:2512.11437](https://arxiv.org/abs/2512.11437) find
  13. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, and 1 others. 2024. The llama 3 herd of models. arXiv preprint arXiv:2407.21783.
  14. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, and 1 others. 2024. The llama 3 herd of models. arXiv e-prints, pages arXiv–2407.
  15. Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, and 1 others. 2025. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948.
  16. Yubin Ge, Devamanyu Hazarika, Yang Liu, and Mahdi Namazifar. 2023. Supervised fine-tuning of large language models on human demonstrations through the lens of memorization. find
  17. Quan Guo, Shuai Cao, and Zhang Yi. 2022. A medical question answering system using large language models and knowledge graphs. International Journal of Intelligent Systems, 37(11):8548–8564. find
  18. Soumya Suvra Ghosal, Vaibhav Singh, Akash Ghosh, Soumyabrata Pal, Subhadip Baidya, Sriparna Saha, and Dinesh Manocha. 2025. Relic: Enhancing reward model generalization for low-resource indic languages with few-shot examples. arXiv preprint arXiv:2506.16502. find
  19. Akash Ghosh, Arkadeep Acharya, Raghav Jain, Sriparna Saha, Aman Chadha, and Setu Sinha. 2024a. Clipsyntel: clip and llm synergy for multimodal question summarization in healthcare. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pages 22031–22039. find
  20. Akash Ghosh, Arkadeep Acharya, Prince Jha, Sriparna Saha, Aniket Gaudgaul, Rajdeep Majumdar, Aman Chadha, Raghav Jain, Setu Sinha, and Shivani Agarwal. 2024b. Medsumm: A multimodal approach to summarizing code-mixed hindi-english clinical queries. In European Conference on Information Retrieval, pag… find
  21. Akash Ghosh, Arkadeep Acharya, Sriparna Saha, Gaurav Pandey, Dinesh Raghu, and Setu Sinha. 2024c. Healthalignsumm: Utilizing alignment for multimodal summarization of code-mixed healthcare dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 11546– 11560. find
  22. Akash Ghosh, Tajamul Ashraf, Rishu Kumar Singh, Numan Saeed, Sriparna Saha, Xiuying Chen, and Salman Khan. 2026. Carepilot: A multi-agent framework for long-horizon computer task automation in healthcare. arXiv preprint arXiv:2603.24157. — [arXiv:2603.24157](https://arxiv.org/abs/2603.24157) find
  23. Katharina Hämmerl, Björn Deiseroth, Patrick Schramowski, Jindřich Libovickỳ, Constantin A Rothkopf, Alexander Fraser, and Kristian Kersting. 2022. Speaking multiple languages affects the moral bias of language models. arXiv preprint arXiv:2211.07733. find
  24. Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, Paul Grundmann, Tom Oberhauser, Alexei Figueroa, Alexander Löser, Daniel Truhn, and Keno K. Bressem. 2025. Medalpaca – an open-source collection of medical conversational ai models and training data. Preprint, arXiv:2304.08247.
  25. Jie Huang and Kevin Chen-Chuan Chang. 2022. Towards reasoning in large language models: A survey. arXiv preprint arXiv:2212.10403.
  26. Zhen Huang, Haoyang Zou, Xuefeng Li, Yixiu Liu, Yuxiang Zheng, Ethan Chern, Shijie Xia, Yiwei Qin, Weizhe Yuan, and Pengfei Liu. 2024. O1 replication journey–part 2: Surpassing o1-preview through simple distillation, big progress or bitter lesson? arXiv preprint arXiv:2411.16489. find
  27. Jaedong Hwang, Kumar Tanmay, Seok-Jin Lee, Ayush Agrawal, Hamid Palangi, Kumar Ayush, Ila Fiete, and Paul Pu Liang. 2025. Learn globally, speak locally: Bridging the gaps in multilingual reasoning. arXiv preprint arXiv:2507.05418. — [arXiv:2507.05418](https://arxiv.org/abs/2507.05418) find
  28. Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, and 1 others. 2024. Openai o1 system card. arXiv preprint arXiv:2412.16720.
  29. Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,… find
  30. Di Jin, Eileen Pan, Nassim Oufattole, Wei-Hung Weng, Hanyi Fang, and Peter Szolovits. 2021. What disease does this patient have? a large-scale open domain question answering dataset from medical exams. Applied Sciences, 11(14):6421.
  31. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. Large language models are zero-shot reasoners. Advances in neural information processing systems, 35:22199– 22213.
  32. Yanis Labrak, Adrien Bazoge, Emmanuel Morin, Pierre-Antoine Gourraud, Mickael Rouvier, and Richard Dufour. 2024. Biomistral: A collection of open-source pretrained large language models for medical domains. Preprint, arXiv:2402.10373.
  33. Saama AI Labs. 2024. Openbiollm: Llama3-based biomedical large language model. https://huggingface.co/aaditya/ Model card. Paper Llama3-OpenBioLLM-70B. in preparation. ↗
  34. Nathan Lambert, Valentina Pyatkin, Jacob Morrison, Lester James Validad Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, and 1 others. 2025. Rewardbench: Evaluating reward models for language modeling. In Findings of the Association for Computational Linguis… find
  35. Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, and 1 others. 2022. Competition-level code generation with alphacode. Science, 378(6624):1092–1097.
  36. Trung Quoc Luong, Xinbo Zhang, Zhanming Jie, Peng Sun, Xiaoran Jin, and Hang Li. 2024. Reft: Reasoning with reinforced fine-tuning. arXiv preprint arXiv:2401.08967.
  37. Farah Magrabi, Elske Ammenwerth, Jytte Brender McNair, Nicolet F De Keizer, Hannele Hyppönen, Pirkko Nykänen, Michael Rigby, Philip J Scott, Tuu-likki Vehko, Zoie Shui-Yee Wong, and 1 others. 2019. Artificial intelligence in clinical decision support: challenges for evaluating ai and practical impli… find
  38. Arijit Maji, Raghvendra Kumar, Akash Ghosh, Ne-mil Shah, Abhilekh Borah, Vanshika Shah, Nishant Mishra, Sriparna Saha, and 1 others. 2025. Drishtikon: A multimodal multilingual benchmark for testing language models’ understanding on indian culture. In Proceedings of the 2025 Conference on Empirical…
  39. Xuan-Phi Nguyen, Sharifah Mahani Aljunied, Shafiq Joty, and Lidong Bing. 2023. Democratizing llms for low-resource languages by leveraging their english dominant abilities with linguistically-diverse prompts. arXiv preprint arXiv:2306.11372.
  40. Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz. 2023. Capabilities of gpt-4 on medical challenge problems. arXiv preprint arXiv:2303.13375.
  41. Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems, 35:27730–27744.
  42. Aaron Parisi, Yao Zhao, and Noah Fiedel. 2022. Talm: Tool augmented language models. arXiv preprint arXiv:2205.12255.
  43. Vimla L Patel, José F Arocha, and Jiajie Zhang. 2005. Thinking and reasoning in medicine. The Cambridge handbook of thinking and reasoning, 14:727–750. find
  44. Valentin Liévin, Christoffer Egeberg Hother, Andreas Geert Motzfeldt, and Ole Winther. 2024. Can large language models reason about medical questions? Patterns, 5(3). find
  45. Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, Weixiong Lin, Haicheng Wang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2024. Towards building multilingual language model for medicine. Nature Communications, 15(1):8384.
  46. Aixin Liu, Bei Feng, Bing Xue, Bingxuan Wang, Bochao Wu, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, and 1 others. 2024a. Deepseek-v3 technical report. arXiv preprint arXiv:2412.19437. find
  47. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. Advances in Neural Information Processing Systems, 36:68539–68551.
  48. Lei Liu, Xiaoyan Yang, Junchi Lei, Yue Shen, Jian Wang, Peng Wei, Zhixuan Chu, Zhan Qin, and Kui Ren. 2024b. A survey on medical large language models: Technology, application, trustworthiness, and future directions. arXiv preprint arXiv:2406.03712. — [arXiv:2406.03712](https://arxiv.org/abs/2406.03712) find
  49. Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Ol-shvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, and Rama Chellappa. 2024. Addressing cognitive bias in medical language models. arXiv preprint arXiv:2402.08113. — [arXiv:2402.08113](https://arxiv.org/abs/2402.08113) find
  50. Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Yang Wu, and 1 others. 2024. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300.
  51. Pat Verga, Sebastian Hofstatter, Sophia Althammer, Yixuan Su, Aleksandra Piktus, Arkady Arkhangorodsky, Minjie Xu, Naomi White, and Patrick Lewis. 2024. Replacing judges with juries: Evaluating llm generations with a panel of diverse models. arXiv preprint arXiv:2404.18796.
  52. Shuaijie She, Wei Zou, Shujian Huang, Wenhao Zhu, Xiang Liu, Xiang Geng, and Jiajun Chen. 2024. Mapo: Advancing multilingual reasoning through multilingual alignment-as-preference optimization. arXiv preprint arXiv:2401.06838.
  53. Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, and 1 others. 2022. Super-naturalinstructions: Generalization via declarative instructions on 1600+ nlp tasks. arXiv preprint arXi… find
  54. Zhang Shengyu, Dong Linfeng, Li Xiaoya, Zhang Sen, Sun Xiaofei, Wang Shuhe, Li Jiwei, Runyi Hu, Zhang Tianwei, Fei Wu, and 1 others. 2023. Instruction tuning for large language models: A survey. arXiv preprint arXiv:2308.10792.
  55. Freda Shi, Mirac Suzgun, Markus Freitag, Xuezhi Wang, Suraj Srivats, Soroush Vosoughi, Hyung Won Chung, Yi Tay, Sebastian Ruder, Denny Zhou, and 1 others. 2022. Language models are multilingual chain-of-thought reasoners. arXiv preprint arXiv:2210.03057.
  56. Punit Kumar Singh, Nishant Kumar, Akash Ghosh, Kunal Pasad, Khushi Soni, Manisha Jaishwal, Sriparna Saha, Syukron Abu Ishaq Alfarozi, Asres Temam Abagissa, Kitsuchart Pasupa, and 1 others. 2025. Let’s play across cultures: A large multilingual, multicultural benchmark for assessing language models’… find
  57. Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Mohamed Amin, Le Hou, Kevin Clark, Stephen R Pfohl, Heather Cole-Lewis, and 1 others. 2025. Toward expert-level medical question answering with large language models. Nature Medicine, 31(3):943–950. find
  58. William W Stead. 2018. Clinical implications and challenges of artificial intelligence and deep learning. Jama, 320(11):1107–1108. find
  59. Yi Su, Dian Yu, Linfeng Song, Juntao Li, Haitao Mi, Zhaopeng Tu, Min Zhang, and Dong Yu. 2025. Crossing the reward bridge: Expanding rl with verifiable rewards across diverse domains. arXiv preprint arXiv:2503.23829. — [arXiv:2503.23829](https://arxiv.org/abs/2503.23829) find
  60. Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023. Stanford alpaca: An instruction-following llama model. find
  61. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, and 1 others. 2024. Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv:2408.00118. find
  62. Mistral AI Team. 2024. Un ministral, des ministraux. Accessed: 2025-12-24. find
  63. Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in neural information processing systems, 35:24824– 24837.
  64. Jin Xu, Zhifang Guo, Jinzheng He, Hangrui Hu, Ting He, Shuai Bai, Keqin Chen, Jialin Wang, Yang Fan, Kai Dang, and 1 others. 2025. Qwen2. 5-omni technical report. arXiv preprint arXiv:2503.20215. — Qwen2.5-Omni Technical Report
  65. An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, and 22 others. 2024. Qwen2.5 technical report. arXiv preprint arXi… find
  66. Yixin Ye, Zhen Huang, Yang Xiao, Ethan Chern, Shijie Xia, and Pengfei Liu. 2025. Limo: Less is more for reasoning. arXiv preprint arXiv:2502.03387.
  67. Haneul Yoo, Cheonbok Park, Sangdoo Yun, Alice Oh, and Hwaran Lee. 2024. Code-switching curriculum learning for multilingual transfer in llms. arXiv preprint arXiv:2411.02460. find
  68. H Zhang, J Chen, F Jiang, F Yu, Z Chen, J Li, G Chen, X Wu, Z Zhang, Q Xiao, and 1 others. Huatuogpt, towards taming language model to be a doctor. arxiv (2023). arXiv preprint arXiv:2305.15075.
  69. Kaiyan Zhang, Sihang Zeng, Ermo Hua, Ning Ding, Zhang-Ren Chen, Zhiyuan Ma, Haoxin Li, Ganqu Cui, Biqing Qi, Xuekai Zhu, and 1 others. 2024. Ultramedical: Building specialized generalists in biomedicine. Advances in Neural Information Processing Systems, 37:26045–26081. find
  70. Guorui Zheng, Xidong Wang, Juhao Liang, Nuo Chen, Yuping Zheng, and Benyou Wang. 2024. Efficiently democratizing medical llms for 50 languages via a mixture of language family experts. Preprint, arXiv:2410.10626. — [arXiv:2410.10626](https://arxiv.org/abs/2410.10626) find
  71. Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, and 1 others. 2023. Judging llm-as-a-judge with mt-bench and chatbot arena. Advances in neural information processing systems, 36:46595–46623.
  72. Chunting Zhou, Pengfei Liu, Puxin Xu, Srinivasan Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, and 1 others. 2023. Lima: Less is more for alignment. Advances in Neural Information Processing Systems, 36:55006–55021. find

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/onyame-2026-cure-med-curriculum-informed-reinforcement/