Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

S. Kullback and R. A. Leibler, “On information and sufficiency,” Annals of Mathematical Statistics , vol. 22, no. 1, pp. 79–86, 1951

1951

Earlier work this paper cites.

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347 , 2017

Original

2017

Earlier work this paper cites.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, pp. 8026–8037, 2019

2019

Earlier work this paper cites.

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PmLR, 2021, pp. 8748–8763

2021

Earlier work this paper cites.

W. Chen, X. Ma, X. Wang, and W. W. Cohen, “Program of thoughts prompting: Disentangling computation from reasoning for numerical reasoning tasks,” arXiv preprint arXiv:2211.12588 , 2022

Original

2022

Earlier work this paper cites.

L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022

2022

Earlier work this paper cites.

Y. Bai, S. Kadavath, S. Kundu, A. Askell, J. Kernion, A. Jones, A. Chen, A. Goldie, A. Mirhoseini, C. McKinnon et al. , “Constitutional ai: Harmlessness from ai feedback,” arXiv preprint arXiv:2212.08073 , 2022

Original

2022

Earlier work this paper cites.

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022

2022

Earlier work this paper cites.

J. Bai, S. Bai, Y. Chu, Z. Cui, K. Dang, X. Deng, Y. Fan, W. Ge, Y. Han, F. Huang et al. , “Qwen technical report,” arXiv preprint arXiv:2309.16609 , 2023

Original

2023

Earlier work this paper cites.

J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat et al. , “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023

Original

2023

Earlier work this paper cites.

M. Moor, Q. Huang, S. Wu, M. Yasunaga, Y. Dalmia, J. Leskovec, C. Zakka, E. P. Reis, and P. Rajpurkar, “Med-flamingo: a multimodal medical few-shot learner,” in Machine Learning for Health (ML4H) . PMLR, 2023, pp. 353–367

2023

Earlier work this paper cites.

X. Zhang, C. Wu, Z. Zhao, W. Lin, Y. Zhang, Y. Wang, and W. Xie, “Pmc-vqa: Visual instruction tuning for medical visual question answering,” arXiv preprint arXiv:2305.10415 , 2023

Original

2023

Earlier work this paper cites.

C. Li, C. Wong, S. Zhang, N. Usuyama, H. Liu, J. Yang, T. Naumann, H. Poon, and J. Gao, “Llava-med: Training a large language-and-vision assistant for biomedicine in one day,” Advances in Neural Information Processing Systems , vol. 36, pp. 28 541–28 564, 2023

2023

Cited alongside, same era.

J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742

2023

Cited alongside, same era.

W. Dai, J. Li, D. Li, A. M. H. Tiong, J. Zhao, W. Wang, B. Li, P. Fung, and S. Hoi, “Instructblip: Towards general-purpose vision-language models with instruction tuning,” 2023. [Online]. Available: https://arxiv.org/abs/2305.06500

Original

2023

Cited alongside, same era.

H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” Advances in neural information processing systems , vol. 36, pp. 34 892–34 916, 2023

2023

Cited alongside, same era.

W. Li, C. Qu, X. Chen, P. R. Bassi, Y. Shi, Y. Lai, Q. Yu, H. Xue, Y. Chen, X. Lin et al. , “Abdomenatlas: A large-scale, detailed-annotated, & multi-center dataset for efficient transfer learning and open algorithmic benchmarking,” Medical Image Analysis , vol. 97, p. 103285, 2024

2024

Later among the works it cites.

Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Y. K. Li, Y. Wu, and D. Guo, “Deepseekmath: Pushing the limits of mathematical reasoning in open language models,” 2024. [Online]. Available: https://arxiv.org/abs/2402.03300

Original

2024

Later among the works it cites.

P. Wang, S. Bai, S. Tan, S. Wang, Z. Fan, J. Bai, K. Chen, X. Liu, J. Wang, W. Ge et al. , “Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution,” arXiv preprint arXiv:2409.12191 , 2024

Original

2024

Later among the works it cites.

Y. Hu, T. Li, Q. Lu, W. Shao, J. He, Y. Qiao, and P. Luo, “Omnimedvqa: A new large-scale comprehensive evaluation benchmark for medical lvlm,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 22 170–22 183