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Recent advances in large language models have significantly improved textual reasoning through the effective use of Chain-of-Thought (CoT) and reinforcement learning.
Microsoft coco captions: Data collection and evaluation server
Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollár, and C Lawrence Zitnick · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Plotqa: Reasoning over scientific plots
Nitesh Methani, Pritha Ganguly, Mitesh M Khapra, and Pratyush Kumar · 2020
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Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning
Pan Lu, Liang Qiu, Jiaqi Chen, Tony Xia, Yizhou Zhao, Wei Zhang, Zhou Yu, Xiaodan Liang, and Song-Chun Zhu · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Florence: A new foundation model for computer vision
Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al · 2021
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Flamingo: a visual language model for few-shot learning
Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, et al · 2022
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Scaling up vision-language pre-training for image captioning
Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang · 2022
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Chartqa: A benchmark for question answering about charts with visual and logical reasoning
Ahmed Masry, Do Xuan Long, Jia Qing Tan, Shafiq Joty, and Enamul Hoque · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Coca: Contrastive captioners are image-text foundation models
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu · 2022
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Qwen-vl: A frontier large vision-language model with versatile abilities
Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou · 2023
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Dreamllm: Synergistic multimodal comprehension and creation
Runpei Dong, Chunrui Han, Yuang Peng, Zekun Qi, Zheng Ge, Jinrong Yang, Liang Zhao, Jianjian Sun, Hongyu Zhou, Haoran Wei, et al · 2023
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Super-clevr: A virtual benchmark to diagnose domain robustness in visual reasoning
Zhuowan Li, Xingrui Wang, Elias Stengel-Eskin, Adam Kortylewski, Wufei Ma, Benjamin Van Durme, and Alan L Yuille · 2023
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Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe · 2023
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Visual chain of thought: bridging logical gaps with multimodal infillings
Daniel Rose, Vaishnavi Himakunthala, Andy Ouyang, Ryan He, Alex Mei, Yujie Lu, Michael Saxon, Chinmay Sonar, Diba Mirza, and William Yang Wang · 2023
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Aligning large multimodal models with factually augmented rlhf
Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liang-Yan Gui, Yu-Xiong Wang, Yiming Yang, et al · 2023
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Gemini: A family of highly capable multimodal models, 2023
Google Gemini Team · 2023
Cited alongside, same era.
Cogvlm: Visual expert for pretrained language models
Weihan Wang, Qingsong Lv, Wenmeng Yu, Wenyi Hong, Ji Qi, Yan Wang, Junhui Ji, Zhuoyi Yang, Lei Zhao, Xixuan Song, et al · 2023
Cited alongside, same era.
The dawn of lmms: Preliminary explorations with gpt-4v (ision)
Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, and Lijuan Wang · 2023
Cited alongside, same era.
Mm-vet: Evaluating large multimodal models for integrated capabilities, 2023
Scaling llm test-time compute optimally can be more effective than scaling model parameters
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
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Cambrian-1: A fully open, vision-centric exploration of multimodal llms
Peter Tong, Ellis Brown, Penghao Wu, Sanghyun Woo, Adithya Jairam Vedagiri IYER, Sai Charitha Akula, Shusheng Yang, Jihan Yang, Manoj Middepogu, Ziteng Wang, et al · 2024
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Charxiv: Charting gaps in realistic chart understanding in multimodal llms
Zirui Wang, Mengzhou Xia, Luxi He, Howard Chen, Yitao Liu, Richard Zhu, Kaiqu Liang, Xindi Wu, Haotian Liu, Sadhika Malladi, et al · 2024
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Mind’s eye of llms: Visualization-of-thought elicits spatial reasoning in large language models
Wenshan Wu, Shaoguang Mao, Yadong Zhang, Yan Xia, Li Dong, Lei Cui, and Furu Wei · 2024
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Scaling inference-time search with vision value model for improved visual comprehension
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Weihao Yu, Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Zicheng Liu, Xinchao Wang, and Lijuan Wang · 2023
Cited alongside, same era.
https://openai.com/index/learning-to-reason-with-llms/ , 2024
Learning to reason with llms · 2024
Cited alongside, same era.
Large language monkeys: Scaling inference compute with repeated sampling
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V Le, Christopher Ré, and Azalia Mirhoseini · 2024
Cited alongside, same era.
Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks
Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, et al · 2024
Cited alongside, same era.
Molmo and pixmo: Open weights and open data for state-of-the-art vision-language models
Matt Deitke, Christopher Clark, Sangho Lee, Rohun Tripathi, Yue Yang, et al · 2024
Cited alongside, same era.
Blink: Multimodal large language models can see but not perceive
Xingyu Fu, Yushi Hu, Bangzheng Li, Yu Feng, Haoyu Wang, Xudong Lin, Dan Roth, Noah A Smith, Wei-Chiu Ma, and Ranjay Krishna · 2024
Cited alongside, same era.
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al · 2024
Cited alongside, same era.
Mammoth-vl: Eliciting multimodal reasoning with instruction tuning at scale
Jarvis Guo, Tuney Zheng, Yuelin Bai, Bo Li, Yubo Wang, King Zhu, Yizhi Li, Graham Neubig, Wenhu Chen, and Xiang Yue · 2024
Cited alongside, same era.
Wang Xiyao, Yang Zhengyuan, Li Linjie, Lu Hongjin, Xu Yuancheng, Lin Chung-Ching Lin, Lin Kevin, Huang Furong, and Wang Lijuan · 2024
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Llava-cot: Let vision language models reason step-by-step
Guowei Xu, Peng Jin, Hao Li, Yibing Song, Lichao Sun, and Li Yuan · 2024
Later among the works it cites.
An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, et al · 2024
Later among the works it cites.
Multimodal chain-of-thought reasoning in language models
Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, and Alex Smola · 2024
Later among the works it cites.
Aligning modalities in vision large language models via preference fine-tuning
Yiyang Zhou, Chenhang Cui, Rafael Rafailov, Chelsea Finn, and Huaxiu Yao · 2024
Later among the works it cites.
R1-v: Reinforcing super generalization ability in vision language models with less than $3
Liang Chen, Lei Li, Haozhe Zhao, Yifan Song, and Vinci · 2025
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Openvlthinker: An early exploration to complex vision-language reasoning via iterative self-improvement, 2025
Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, and Kai-Wei Chang · 2025
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Refocus: Visual editing as a chain of thought for structured image understanding
Xingyu Fu, Minqian Liu, Zhengyuan Yang, John Corring, Yijuan Lu, Jianwei Yang, Dan Roth, Dinei Florencio, and Cha Zhang · 2025
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Can mllms reason in multimodality? emma: An enhanced multimodal reasoning benchmark
Yunzhuo Hao, Jiawei Gu, Huichen Will Wang, Linjie Li, Zhengyuan Yang, Lijuan Wang, and Yu Cheng · 2025
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Vision-r1: Incentivizing reasoning capability in multimodal large language models, 2025
Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaohui Lin · 2025
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Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning
Fanqing Meng, Lingxiao Du, Zongkai Liu, Zhixiang Zhou, Quanfeng Lu, Daocheng Fu, Botian Shi, Wenhai Wang, Junjun He, Kaipeng Zhang, et al · 2025
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Lmm-r1: Empowering 3b lmms with strong reasoning abilities through two-stage rule-based rl
Yingzhe Peng, Gongrui Zhang, Miaosen Zhang, Zhiyuan You, Jie Liu, Qipeng Zhu, Kai Yang, Xingzhong Xu, Xin Geng, and Xu Yang · 2025
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Codeelo: Benchmarking competition-level code generation of llms with human-comparable elo ratings
Shanghaoran Quan, Jiaxi Yang, Bowen Yu, Bo Zheng, Dayiheng Liu, An Yang, Xuancheng Ren, Bofei Gao, Yibo Miao, Yunlong Feng, et al · 2025
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Easyr1: An efficient, scalable, multi-modality rl training framework
Yaowei Zheng, Junting Lu, Shenzhi Wang, Zhangchi Feng, Dongdong Kuang, and Yuwen Xiong · 2025
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