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home / papers / 2025 / GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning

Paper · 2025 · GLM-V Team, Wenyi Hong, Wenmeng Yu et al.

Summary

The paper addresses a gap in open-source vision-language models (VLMs): no openly available model reliably beat comparably sized non-reasoning models across a wide range of multimodal tasks. The GLM-V Team (Zhipu AI & Tsinghua University) built a family of reasoning-focused VLMs using a three-stage pipeline. First, they pre-trained on a large, knowledge-heavy multimodal corpus that includes recaptioned image-text pairs, interleaved academic material, documents, videos and grounding data. Second, supervised fine-tuning taught a standardized reasoning format. Third, a multi-domain RL stage called RLCS paired curriculum learning with difficulty-aware sampling. Across 42 public benchmarks, GLM-4.5V leads open models of its size on nearly every task. It also matches or beats Gemini-2.5-Flash on many tasks, notably coding and GUI agents. The compact GLM-4.1V-9B-Thinking outperforms Qwen2.5-VL-72B on 29 benchmarks. The work matters because it releases strong open checkpoints and reward components. It also shows that RL across many domains can transfer gains between related skills.

Key points

  • Architecture:
    • The vision encoder is initialized from AIMv2-Huge and connects to the LLM through an MLP adapter.
    • The LLM is GLM-4-9B-0414 for the 9B models and GLM-4.5-Air for GLM-4.5V/GLM-4.6V (106B-A12B MoE).
    • The encoder uses 3D convolutions for 2× temporal downsampling of video.
    • Images keep native resolution through 2D-RoPE plus bicubically interpolated position embeddings, handling aspect ratios beyond 200:1 and resolutions above 4K.
    • The LLM uses 3D-RoPE, and timestamp tokens follow each video frame.
  • Pre-training data: curation began from a pool of over 10 billion image-text pairs. The resulting GLM-4.1V-9B-Base shows stronger pass@k on MathVista than a comparable base model.
  • RL findings:
    • RL raises GLM-4.5V's scores by up to +10.6%.
    • Training on all domains jointly beats single-domain RL for STEM, OCR & Chart and general VQA, but not for grounding or GUI agents.
    • GUI-agent and grounding training help each other.
    • Reward-signal weaknesses can derail the whole run.
  • Released models: GLM-4.1V-9B-Thinking, GLM-4.1V-9B-Base, GLM-4.5V, GLM-4.6V-Flash and GLM-4.6V, with 128K context and native tool use for the 4.6V series. Code is at github.com/zai-org/GLM-V.
  • Stated limitations:
    • Outcome-only rewards can reinforce flawed reasoning chains.
    • RL training is sensitive to small setup changes.
    • Perception errors appear in cluttered or occluded scenes.

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

📚 Shelves 3The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksComputer Vision and Pattern Recognition · 1,282 works

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 510 worksVision & language · 269 works

Cite

Cite as GLM-V Team, Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, Shuaiqi Duan, Weihan Wang, Yan Wang, Yean Cheng, Zehai He, Zhe Su, Zhen Yang, Ziyang Pan, Aohan Zeng, Baoxu Wang, Bin Chen, Boyan Shi, Changyu Pang, Chenhui Zhang, Da Yin, Fan Yang, Guoqing Chen, Haochen Li, Jiale Zhu, Jiali Chen, Jiaxing Xu, Jiazheng Xu, Jing Chen, Jinghao Lin, Jinhao Chen, Jinjiang Wang, Junjie Chen, Leqi Lei, Letian Gong, Leyi Pan, Mingdao Liu, Mingde Xu, Mingzhi Zhang, Qinkai Zheng, Ruiliang Lyu, Shangqin Tu, Sheng Yang, Shengbiao Meng, Shi Zhong, Shiyu Huang, Shuyuan Zhao, Siyan Xue, Tianshu Zhang, Tianwei Luo, Tianxiang Hao, Tianyu Tong, Wei Jia, Wenkai Li, Xiao Liu, Xiaohan Zhang, Xin Lyu, Xinyu Zhang, Xinyue Fan, Xuancheng Huang, Yadong Xue, Yanfeng Wang, Yanling Wang, Yanzi Wang, Yifan An, Yifan Du, Yiheng Huang, Yilin Niu, Yiming Shi, Yu Wang, Yuan Wang, Yuanchang Yue, Yuchen Li, Yusen Liu, Yutao Zhang, Yuting Wang, Yuxuan Zhang, Zhao Xue, Zhengxiao Du, Zhenyu Hou, Zihan Wang, Peng Zhang, Debing Liu, Bin Xu, Juanzi Li, Minlie Huang, Yuxiao Dong and Jie Tang. 2025. GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning. arXiv:2507.01006.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2507.01006 · arXiv 2507.01006

@misc{team2025glm,
  title = {{GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning}},
  author = {GLM-V Team, Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, Shuaiqi Duan, Weihan Wang, Yan Wang, Yean Cheng, Zehai He, Zhe Su, Zhen Yang, Ziyang Pan, Aohan Zeng, Baoxu Wang, Bin Chen, Boyan Shi, Changyu Pang, Chenhui Zhang, Da Yin, Fan Yang, Guoqing Chen, Haochen Li, Jiale Zhu, Jiali Chen, Jiaxing Xu, Jiazheng Xu, Jing Chen, Jinghao Lin, Jinhao Chen, Jinjiang Wang, Junjie Chen, Leqi Lei, Letian Gong, Leyi Pan, Mingdao Liu, Mingde Xu, Mingzhi Zhang, Qinkai Zheng, Ruiliang Lyu, Shangqin Tu, Sheng Yang, Shengbiao Meng, Shi Zhong, Shiyu Huang, Shuyuan Zhao, Siyan Xue, Tianshu Zhang, Tianwei Luo, Tianxiang Hao, Tianyu Tong, Wei Jia, Wenkai Li, Xiao Liu, Xiaohan Zhang, Xin Lyu, Xinyu Zhang, Xinyue Fan, Xuancheng Huang, Yadong Xue, Yanfeng Wang, Yanling Wang, Yanzi Wang, Yifan An, Yifan Du, Yiheng Huang, Yilin Niu, Yiming Shi, Yu Wang, Yuan Wang, Yuanchang Yue, Yuchen Li, Yusen Liu, Yutao Zhang, Yuting Wang, Yuxuan Zhang, Zhao Xue, Zhengxiao Du, Zhenyu Hou, Zihan Wang, Peng Zhang, Debing Liu, Bin Xu, Juanzi Li, Minlie Huang, Yuxiao Dong and Jie Tang},
  year = {2025},
  doi = {10.48550/arxiv.2507.01006},
  eprint = {2507.01006},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2507.01006}
}

References

73 references, 33 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Flame-code-vlm. https://github.com/Flame-Code-VLM/Flame-Code-VLM. ↗
  2. Geobench. https://github.com/ccmdi/geobench. ↗
  3. A. Awadalla, L. Xue, O. Lo, M. Shu, H. Lee, E. Guha, S. Shen, M. Awadalla, S. Savarese, C. Xiong, et al. Mint-1t: Scaling open-source multimodal data by 10x: A multimodal dataset with one trillion tokens. Advances in Neural Information Processing Systems, 37:36805–36828, 2024. find
  4. S. Bai, K. Chen, X. Liu, J. Wang, W. Ge, S. Song, K. Dang, P. Wang, S. Wang, J. Tang, et al. Qwen2. 5-vl technical report. arXiv preprint arXiv:2502.13923, 2025.
  5. L. Blecher, G. Cucurull, T. Scialom, and R. Stojnic. Nougat: Neural optical understanding for academic documents. arXiv preprint arXiv:2308.13418, 2023. find
  6. J. Chen, F. Wei, J. Zhao, S. Song, B. Wu, Z. Peng, S.-H. G. Chan, and H. Zhang. Revisiting referring expression comprehension evaluation in the era of large multimodal models. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 513–524, 2025. find
  7. L. Chen, J. Li, X. Dong, P. Zhang, Y. Zang, Z. Chen, H. Duan, J. Wang, Y. Qiao, D. Lin, et al. Are we on the right way for evaluating large vision-language models? arXiv preprint arXiv:2403.20330, 2024.
  8. A. Fang, A. M. Jose, A. Jain, L. Schmidt, A. Toshev, and V. Shankar. Data filtering networks. arXiv preprint arXiv:2309.17425, 2023. find
  9. E. Fini, M. Shukor, X. Li, P. Dufter, M. Klein, D. Haldimann, S. Aitharaju, V. G. T. da Costa, L. Béthune, Z. Gan, et al. Multimodal autoregressive pre-training of large vision encoders. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 9641–9654, 2025. find
  10. C. Fu, Y. Dai, Y. Luo, L. Li, S. Ren, R. Zhang, Z. Wang, C. Zhou, Y. Shen, M. Zhang, et al. Video-mme: The first-ever comprehensive evaluation benchmark of multi-modal llms in video analysis. arXiv:2405.21075, 2024.
  11. X. Fu, Y. Hu, B. Li, Y. Feng, H. Wang, X. Lin, D. Roth, N. A. Smith, W.-C. Ma, and R. Krishna. Blink: Multimodal large language models can see but not perceive. arXiv preprint arXiv:2404.12390, 2024. find
  12. S. Y. Gadre, G. Ilharco, A. Fang, J. Hayase, G. Smyrnis, T. Nguyen, R. Marten, M. Wortsman, D. Ghosh, J. Zhang, et al. Datacomp: In search of the next generation of multimodal datasets. Advances in Neural Information Processing Systems, 36:27092–27112, 2023. find
  13. T. GLM, A. Zeng, B. Xu, B. Wang, C. Zhang, D. Yin, D. Rojas, G. Feng, H. Zhao, H. Lai, et al. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.12793, 2024.
  14. J. Gu, X. Meng, G. Lu, L. Hou, N. Minzhe, X. Liang, L. Yao, R. Huang, W. Zhang, X. Jiang, et al. Wukong: A 100 million large-scale chinese cross-modal pre-training benchmark. Advances in Neural Information Processing Systems, 35:26418–26431, 2022. find
  15. T. Guan, F. Liu, X. Wu, R. Xian, Z. Li, X. Liu, X. Wang, L. Chen, F. Huang, Y. Yacoob, et al. Hallusionbench: an advanced diagnostic suite for entangled language hallucination and visual illusion in large vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Patter…
  16. D. Guo, F. Wu, F. Zhu, F. Leng, G. Shi, H. Chen, H. Fan, J. Wang, J. Jiang, J. Wang, et al. Seed1. 5-vl technical report. arXiv preprint arXiv:2505.07062, 2025.
  17. D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025.
  18. H. He, W. Yao, K. Ma, W. Yu, Y. Dai, H. Zhang, Z. Lan, and D. Yu. Webvoyager: Building an end-to-end web agent with large multimodal models. arXiv preprint arXiv:2401.13919, 2024.
  19. W. Hong*, Y. Cheng*, Z. Yang*, W. Wang, L. Wang, X. Gu, S. Huang, Y. Dong, and J. Tang. Motionbench: Benchmarking and improving fine-grained video motion understanding for vision language models, 2024. find
  20. W. Hong, W. Wang, M. Ding, W. Yu, Q. Lv, Y. Wang, Y. Cheng, S. Huang, J. Ji, Z. Xue, et al. Cogvlm2: Visual language models for image and video understanding. arXiv preprint arXiv:2408.16500, 2024.
  21. W. Hong, W. Wang, Q. Lv, J. Xu, W. Yu, J. Ji, Y. Wang, Z. Wang, Y. Dong, M. Ding, et al. Cogagent: A visual language model for gui agents. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14281–14290, 2024.
  22. K. Hu, P. Wu, F. Pu, W. Xiao, Y. Zhang, X. Yue, B. Li, and Z. Liu. Video-mmmu: Evaluating knowledge acquisition from multi-discipline professional videos. 2025. find
  23. A. Jaech, A. Kalai, A. Lerer, A. Richardson, A. El-Kishky, A. Low, A. Helyar, A. Madry, A. Beutel, A. Carney, et al. Openai o1 system card. arXiv preprint arXiv:2412.16720, 2024.
  24. M. Jia, Z. Qi, S. Zhang, W. Zhang, X. Yu, J. He, H. Wang, and L. Yi. Omnispatial: Towards comprehensive spatial reasoning benchmark for vision language models. arXiv preprint arXiv:2506.03135, 2025. — [arXiv:2506.03135](https://arxiv.org/abs/2506.03135) find
  25. S. Kazemzadeh, V. Ordonez, M. Matten, and T. Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 787–798, 2014.
  26. A. Kembhavi, M. Salvato, E. Kolve, M. Seo, H. Hajishirzi, and A. Farhadi. A diagram is worth a dozen images. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pages 235–251. Springer, 2016.
  27. 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, pages 19730–19742. PMLR, 2023.
  28. K. Li, Y. Wang, Y. He, Y. Li, Y. Wang, Y. Liu, Z. Wang, J. Xu, G. Chen, P. Luo, L. Wang, and Y. Qiao. MVBench: A comprehensive multi-modal video understanding benchmark, 2023.
  29. Q. Li, Z. Chen, W. Wang, W. Wang, S. Ye, Z. Jin, G. Chen, Y. He, Z. Gao, E. Cui, et al. Omnicorpus: A unified multimodal corpus of 10 billion-level images interleaved with text. arXiv preprint arXiv:2406.08418, 2024. — [arXiv:2406.08418](https://arxiv.org/abs/2406.08418) find
  30. Y. Liu, H. Duan, Y. Zhang, B. Li, S. Zhang, W. Zhao, Y. Yuan, J. Wang, C. He, Z. Liu, K. Chen, and D. Lin. Mmbench: Is your multi-modal model an all-around player? arXiv:2307.06281, 2023.
  31. Y. Liu, Z. Li, M. Huang, B. Yang, W. Yu, C. Li, X.-C. Yin, C.-L. Liu, L. Jin, and X. Bai. Ocrbench: on the hidden mystery of ocr in large multimodal models. Science China Information Sciences, 67(12), Dec. 2024.
  32. P. Lu, H. Bansal, T. Xia, J. Liu, C. Li, H. Hajishirzi, H. Cheng, K.-W. Chang, M. Galley, and J. Gao. Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255, 2023.
  33. Y. Ma, L. Du, X. Shen, S. Chen, P. Li, Q. Ren, L. Ma, Y. Dai, P. Liu, and J. Yan. One rl to see them all: Visual triple unified reinforcement learning, 2025. find
  34. Y. Ma, Y. Zang, L. Chen, M. Chen, Y. Jiao, X. Li, X. Lu, Z. Liu, Y. Ma, X. Dong, P. Zhang, L. Pan, Y.-G. Jiang, J. Wang, Y. Cao, and A. Sun. Mmlongbench-doc: Benchmarking long-context document understanding with visualizations, 2024. find
  35. A. Masry, M. S. Islam, M. Ahmed, A. Bajaj, F. Kabir, A. Kartha, M. T. R. Laskar, M. Rahman, S. Rahman, M. Shahmohammadi, M. Thakkar, M. R. Parvez, E. Hoque, and S. Joty. Chartqapro: A more diverse and challenging benchmark for chart question answering, 2025. find
  36. R. Qiao, Q. Tan, G. Dong, M. Wu, C. Sun, X. Song, Z. GongQue, S. Lei, Z. Wei, M. Zhang, et al. We-math: Does your large multimodal model achieve human-like mathematical reasoning? arXiv preprint arXiv:2407.01284, 2024. find
  37. C. Rawles, S. Clinckemaillie, Y. Chang, J. Waltz, G. Lau, M. Fair, A. Li, W. Bishop, W. Li, F. Campbell-Ajala, et al. Androidworld: A dynamic benchmarking environment for autonomous agents. arXiv:2405.14573, 2024. find
  38. J. Roberts, M. R. Taesiri, A. Sharma, A. Gupta, S. Roberts, I. Croitoru, S.-V. Bogolin, J. Tang, F. Langer, V. Raina, et al. Zerobench: An impossible visual benchmark for contemporary large multimodal models. arXiv preprint arXiv:2502.09696, 2025. — [arXiv:2502.09696](https://arxiv.org/abs/2502.09696) find
  39. C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. Advances in neural information processing systems, 35:25278– 25294, 2022.
  40. R. Shao, S. S. Li, R. Xin, S. Geng, Y. Wang, S. Oh, S. S. Du, N. Lambert, S. Min, R. Krishna, et al. Spurious rewards: Rethinking training signals in rlvr. arXiv preprint arXiv:2506.10947, 2025. find
  41. Z. Shao, P. Wang, Q. Zhu, R. Xu, J. Song, X. Bi, H. Zhang, M. Zhang, Y. Li, Y. Wu, et al. Deepseekmath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300, 2024.
  42. C. Si, Y. Zhang, Z. Yang, R. Liu, and D. Yang. Design2code: How far are we from automating front-end engineering?, 2024. URL https://arxiv.org/abs/2403, 3163, 2024. ↗
  43. J. Su, Y. Lu, S. Pan, A. Murtadha, B. Wen, and Y. Liu. Roformer: Enhanced transformer with rotary position embedding. arXiv preprint arXiv:2104.09864, 2021.
  44. L. Tang, G. Kim, X. Zhao, T. Lake, W. Ding, F. Yin, P. Singhal, M. Wadhwa, Z. L. Liu, Z. Sprague, et al. Chartmuseum: Testing visual reasoning capabilities of large vision-language models. arXiv preprint arXiv:2505.13444, 2025. — [arXiv:2505.13444](https://arxiv.org/abs/2505.13444) find
  45. C. Team, Z. Yue, Z. Lin, Y. Song, W. Wang, S. Ren, S. Gu, S. Li, P. Li, L. Zhao, L. Li, K. Bao, H. Tian, H. Zhang, G. Wang, D. Zhu, Cici, C. He, B. Ye, B. Shen, Z. Zhang, Z. Jiang, Z. Zheng, Z. Song, Z. Luo, Y. Yu, Y. Wang, Y. Tian, Y. Tu, Y. Yan, Y. Huang, X. Wang, X. Xu, X. Song, X. Zhang, X. Yong… find
  46. G. Team, R. Anil, S. Borgeaud, Y. Wu, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican, D. Silver, S. Petrov, M. Johnson, I. Antonoglou, J. Schrittwieser, A. Glaese, J. Chen, E. Pitler, T. Lillicrap, A. Lazaridou, O. Firat, J. Molloy, M. Isard, P. R. Barham, T. Hennig…
  47. G. Team, A. Kamath, J. Ferret, S. Pathak, N. Vieillard, R. Merhej, S. Perrin, T. Matejovicova, A. Ramé, M. Rivière, et al. Gemma 3 technical report. arXiv preprint arXiv:2503.19786, 2025. find
  48. G. R. Team, S. Abeyruwan, J. Ainslie, J.-B. Alayrac, M. G. Arenas, T. Armstrong, A. Balakrishna, R. Baruch, M. Bauza, M. Blokzijl, et al. Gemini robotics: Bringing ai into the physical world. arXiv preprint arXiv:2503.20020, 2025. — [arXiv:2503.20020](https://arxiv.org/abs/2503.20020) find
  49. K. Team, A. Du, B. Yin, B. Xing, B. Qu, B. Wang, C. Chen, C. Zhang, C. Du, C. Wei, C. Wang, D. Zhang, D. Du, D. Wang, E. Yuan, E. Lu, F. Li, F. Sung, G. Wei, G. Lai, H. Zhu, H. Ding, H. Hu, H. Yang, H. Zhang, H. Wu, H. Yao, H. Lu, H. Wang, H. Gao, H. Zheng, J. Li, J. Su, J. Wang, J. Deng, J. Qiu, J.…
  50. S. Tong, E. Brown, P. Wu, S. Woo, M. Middepogu, S. C. Akula, J. Yang, S. Yang, A. Iyer, X. Pan, A. Wang, R. Fergus, Y. LeCun, and S. Xie. Cambrian-1: A fully open, vision-centric exploration of multimodal llms, 2024.
  51. B. Wang, B. Wang, C. Wan, G. Huang, H. Hu, H. Jia, H. Nie, M. Li, N. Chen, S. Chen, et al. Step-3 is large yet affordable: Model-system co-design for cost-effective decoding. arXiv preprint arXiv:2507.19427, 2025. find
  52. F. Wang, X. Fu, J. Y. Huang, Z. Li, Q. Liu, X. Liu, M. D. Ma, N. Xu, W. Zhou, K. Zhang, et al. Muirbench: A comprehensive benchmark for robust multi-image understanding. arXiv preprint arXiv:2406.09411, 2024. — [arXiv:2406.09411](https://arxiv.org/abs/2406.09411) find
  53. H. Wang, X. Li, Z. Huang, A. Wang, J. Wang, T. Zhang, J. Zheng, S. Bai, Z. Kang, J. Feng, et al. Traceable evidence enhanced visual grounded reasoning: Evaluation and methodology. arXiv preprint arXiv:2507.07999, 2025. — [arXiv:2507.07999](https://arxiv.org/abs/2507.07999) find
  54. K. Wang, J. Pan, W. Shi, Z. Lu, M. Zhan, and H. Li. Measuring multimodal mathematical reasoning with math-vision dataset. arXiv:2402.14804, 2024.
  55. M. Wang, S. Sunkara, G. Baechler, J. Lin, Y. Zhu, F. Zubach, L. Shu, and J. Chen. Webquest: A benchmark for multimodal qa on web page sequences, 2024. find
  56. 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.
  57. W. Wang, Z. He, W. Hong, Y. Cheng, X. Zhang, J. Qi, S. Huang, B. Xu, Y. Dong, M. Ding, et al. Lvbench: An extreme long video understanding benchmark. arXiv preprint arXiv:2406.08035, 2024. find
  58. W. Wang, Q. Lv, W. Yu, W. Hong, J. Qi, Y. Wang, J. Ji, Z. Yang, L. Zhao, X. Song, et al. Cogvlm: Visual expert for pretrained language models. arXiv preprint arXiv:2311.03079, 2023.
  59. J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou. Chain-of-thought prompting elicits reasoning in large language models. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, editors, Proc. of Neural Information Processing Systems, 2022.
  60. Y. Xiao, E. Sun, T. Liu, and W. Wang. Logicvista: Multimodal llm logical reasoning benchmark in visual contexts, 2024. find
  61. T. Xie, D. Zhang, J. Chen, X. Li, S. Zhao, R. Cao, J. H. Toh, Z. Cheng, D. Shin, F. Lei, et al. Osworld: Benchmarking multimodal agents for open-ended tasks in real computer environments. Advances in Neural Information Processing Systems, 37:52040–52094, 2025.
  62. H. Xu, S. Xie, X. E. Tan, P.-Y. Huang, R. Howes, V. Sharma, S.-W. Li, G. Ghosh, L. Zettlemoyer, and C. Feichtenhofer. Demystifying clip data. arXiv preprint arXiv:2309.16671, 2023. — [arXiv:2309.16671](https://arxiv.org/abs/2309.16671) find
  63. Y. Xu, H. Dong, L. Wang, D. Sahoo, J. Li, and C. Xiong. Scalable chain of thoughts via elastic reasoning. arXiv preprint arXiv:2505.05315, 2025. — [arXiv:2505.05315](https://arxiv.org/abs/2505.05315) find
  64. C.-H. Yeh, C. Wang, S. Tong, T.-Y. Cheng, R. Wang, T. Chu, Y. Zhai, Y. Chen, S. Gao, and Y. Ma. Seeing from another perspective: Evaluating multi-view understanding in mllms. arXiv preprint arXiv:2504.15280, 2025. — [arXiv:2504.15280](https://arxiv.org/abs/2504.15280) find
  65. Q. Yu, Z. Zhang, R. Zhu, Y. Yuan, X. Zuo, Y. Yue, W. Dai, T. Fan, G. Liu, L. Liu, et al. Dapo: An open-source llm reinforcement learning system at scale. arXiv preprint arXiv:2503.14476, 2025.
  66. X. Yue, Y. Ni, K. Zhang, T. Zheng, R. Liu, G. Zhang, S. Stevens, D. Jiang, W. Ren, Y. Sun, C. Wei, B. Yu, R. Yuan, R. Sun, M. Yin, B. Zheng, Z. Yang, Y. Liu, W. Huang, H. Sun, Y. Su, and W. Chen. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi. In Pro…
  67. X. Yue, T. Zheng, Y. Ni, Y. Wang, K. Zhang, S. Tong, Y. Sun, B. Yu, G. Zhang, H. Sun, Y. Su, W. Chen, and G. Neubig. Mmmu-pro: A more robust multi-discipline multimodal understanding benchmark. arXiv preprint arXiv:2409.02813, 2024. find
  68. H. Zhang, P. Zhang, X. Hu, Y.-C. Chen, L. Li, X. Dai, L. Wang, L. Yuan, J.-N. Hwang, and J. Gao. Glipv2: Unifying localization and vision-language understanding. Proc. of Neural Information Processing Systems, 35:36067–36080, 2022. find
  69. R. Zhang, D. Jiang, Y. Zhang, H. Lin, Z. Guo, P. Qiu, A. Zhou, P. Lu, K.-W. Chang, Y. Qiao, et al. Mathverse: Does your multi-modal llm truly see the diagrams in visual math problems? In European Conference on Computer Vision, pages 169–186. Springer, 2024.
  70. Y. Zhao, L. Xie, H. Zhang, G. Gan, Y. Long, Z. Hu, T. Hu, W. Chen, C. Li, J. Song, Z. Xu, C. Wang, W. Pan, Z. Shangguan, X. Tang, Z. Liang, Y. Liu, C. Zhao, and A. Cohan. Mmvu: Measuring expert-level multi-discipline video understanding, 2025. find
  71. J. Zhu, W. Wang, Z. Chen, Z. Liu, S. Ye, L. Gu, H. Tian, Y. Duan, W. Su, J. Shao, et al. Internvl3: Exploring advanced training and test-time recipes for open-source multimodal models. arXiv preprint arXiv:2504.10479, 2025.
  72. W. Zhu, J. Hessel, A. Awadalla, S. Y. Gadre, J. Dodge, A. Fang, Y. Yu, L. Schmidt, W. Y. Wang, and Y. Choi. Multimodal c4: An open, billion-scale corpus of images interleaved with text. Advances in Neural Information Processing Systems, 36:8958–8974, 2023. find
  73. C. Zou, X. Guo, R. Yang, J. Zhang, B. Hu, and H. Zhang. Dynamath: A dynamic visual benchmark for evaluating mathematical reasoning robustness of vision language models. arXiv preprint arXiv:2411.00836, 2024. find

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