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Improving Multi-modal Large Language Models (MLLMs) in the post-training stage typically relies on supervised fine-tuning (SFT) or reinforcement learning (RL), which require expensive and manually annotated multi-modal data--an ultimately unsustainable resource.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Geoqa: A geometric question answering benchmark towards multimodal numerical reasoning
Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu, Eric Xing, and Liang Lin · 2021
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Measuring mathematical problem solving with the math dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt · 2021
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Large language models can self-improve
Jiaxin Huang, Shixiang Shane Gu, Le Hou, Yuexin Wu, Xuezhi Wang, Hongkun Yu, and Jiawei Han · 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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G-llava: Solving geometric problem with multi-modal large language model
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Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee · 2023
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Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct
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Instructiongpt-4: A 200-instruction paradigm for fine-tuning minigpt-4
Lai Wei, Zihao Jiang, Weiran Huang, and Lichao Sun · 2023
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Metamath: Bootstrap your own mathematical questions for large language models
Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T Kwok, Zhenguo Li, Adrian Weller, and Weiyang Liu · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny · 2023
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Vision-language models can self-improve reasoning via reflection
Kanzhi Cheng, Yantao Li, Fangzhi Xu, Jianbing Zhang, Hao Zhou, and Yang Liu · 2024
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Enhancing large vision language models with self-training on image comprehension
Yihe Deng, Pan Lu, Fan Yin, Ziniu Hu, Sheng Shen, Quanquan Gu, James Y Zou, Kai-Wei Chang, and Wei Wang · 2024
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Self-boosting large language models with synthetic preference data
Qingxiu Dong, Li Dong, Xingxing Zhang, Zhifang Sui, and Furu Wei · 2024
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Omni-math: A universal olympiad level mathematic benchmark for large language models
Bofei Gao, Feifan Song, Zhe Yang, Zefan Cai, Yibo Miao, Qingxiu Dong, Lei Li, Chenghao Ma, Liang Chen, Runxin Xu, et al · 2024
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Infinity-mm: Scaling multimodal performance with large-scale and high-quality instruction data
Shuhao Gu, Jialing Zhang, Siyuan Zhou, Kevin Yu, Zhaohu Xing, Liangdong Wang, Zhou Cao, Jintao Jia, Zhuoyi Zhang, Yixuan Wang, et al · 2024
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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
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Seeking the right question: Towards high-quality visual instruction generation
Xin Huang, Jing Bai, Yeqing Shen, Jia Wang, Zheng Ge, and Osamu Yoshie · 2024
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Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al · 2024
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Aaron Jaech, Adam Kalai, Adam Lerer, Adam Richardson, Ahmed El-Kishky, Aiden Low, Alec Helyar, Aleksander Madry, Alex Beutel, Alex Carney, et al · 2024
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Improving llm reasoning through scaling inference computation with collaborative verification
Zhenwen Liang, Ye Liu, Tong Niu, Xiangliang Zhang, Yingbo Zhou, and Semih Yavuz · 2024
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Dakota Mahan, Duy Van Phung, Rafael Rafailov, Chase Blagden, Nathan Lile, Louis Castricato, Jan-Philipp Fränken, Chelsea Finn, and Alon Albalak · 2024
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Multimath: Bridging visual and mathematical reasoning for large language models
Shuai Peng, Di Fu, Liangcai Gao, Xiuqin Zhong, Hongguang Fu, and Zhi Tang · 2024
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A survey of ai-generated content (aigc)
Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip Yu, and Lichao Sun · 2025
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R1-v: Reinforcing super generalization ability in vision-language models with less than $3
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Yihe Deng, Hritik Bansal, Fan Yin, Nanyun Peng, Wei Wang, and Kai-Wei Chang · 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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Mathruler
hiyouga · 2025
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Runqi Qiao, Qiuna Tan, Guanting Dong, Minhui Wu, Chong Sun, Xiaoshuai Song, Zhuoma GongQue, Shanglin Lei, Zhe Wei, Miaoxuan Zhang, et al · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Christopher D Manning, Stefano Ermon, and Chelsea Finn · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
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Math-llava: Bootstrapping mathematical reasoning for multimodal large language models
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Can i understand what i create? self-knowledge evaluation of large language models
Zhiquan Tan, Lai Wei, Jindong Wang, Xing Xie, and Weiran Huang · 2024
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Dart-math: Difficulty-aware rejection tuning for mathematical problem-solving
Yuxuan Tong, Xiwen Zhang, Rui Wang, Ruidong Wu, and Junxian He · 2024
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To ensemble or not: Assessing majority voting strategies for phishing detection with large language models
Fouad Trad and Ali Chehab · 2024
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Longllava: Scaling multi-modal llms to 1000 images efficiently via a hybrid architecture
Xidong Wang, Dingjie Song, Shunian Chen, Chen Zhang, and Benyou Wang · 2024
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Mmr1: Advancing the frontiers of multimodal reasoning
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Mm-eureka: Exploring visual aha moment with rule-based large-scale reinforcement learning
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Openai gpt-4.5 system card, 2025
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Skywork r1v: Pioneering multimodal reasoning with chain-of-thought
Yi Peng, Xiaokun Wang, Yichen Wei, Jiangbo Pei, Weijie Qiu, Ai Jian, Yunzhuo Hao, Jiachun Pan, Tianyidan Xie, Li Ge, et al · 2025
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Enhancing cognition and explainability of multimodal foundation models with self-synthesized data
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Beyond human data: Aligning multimodal large language models by iterative self-evolution
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A survey on post-training of large language models
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Advancing multimodal reasoning via reinforcement learning with cold start
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Agentic robot: A brain-inspired framework for vision-language-action models in embodied agents
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Easyr1: An efficient, scalable, multi-modality rl training framework
Zheng Yaowei, Lu Junting, Wang Shenzhi, Feng Zhangchi, Kuang Dongdong, and Xiong Yuwen · 2025
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Dapo: An open-source llm reinforcement learning system at scale
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Does reinforcement learning really incentivize reasoning capacity in llms beyond the base model?
Yang Yue, Zhiqi Chen, Rui Lu, Andrew Zhao, Zhaokai Wang, Shiji Song, and Gao Huang · 2025
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R1-zero’s "aha moment" in visual reasoning on a 2b non-sft model
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InImageTrans: Multimodal LLM-based text image machine translation
Fei Zuo, Kehai Chen, Yu Zhang, Zhengshan Xue, and Min Zhang · 2025
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