Built on

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.
K. Yi, C. Gan, Y. Li, P. Kohli, J. Wu, A. Torralba, and J. B. Tenenbaum, “Clevrer: Collision events for video representation and reasoning,” arXiv preprint arXiv:1910.01442 , 2019
Original
2019
Earlier work this paper cites.
L. von Werra, Y. Belkada, L. Tunstall, E. Beeching, T. Thrush, N. Lambert, S. Huang, K. Rasul, and Q. Gallouédec, “Trl: Transformer reinforcement learning,” https://github.com/huggingface/trl , 2020
2020
Earlier work this paper cites.
R. Zheng, S. Dou, S. Gao, Y. Hua, W. Shen, B. Wang, Y. Liu, S. Jin, Q. Liu, Y. Zhou et al. , “Secrets of rlhf in large language models part i: Ppo,” arXiv preprint arXiv:2307.04964 , 2023
Original
2023
Earlier work this paper cites.
F. Shu, L. Zhang, H. Jiang, and C. Xie, “Audio-visual llm for video understanding,” arXiv preprint arXiv:2312.06720 , 2023
Original
2023
Earlier work this paper cites.
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
Original
2024
Earlier work this paper cites.
J. Xiao, A. Yao, Y. Li, and T. S. Chua, “Can i trust your answer? visually grounded video question answering,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Earlier work this paper cites.
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
Original
2024
Earlier work this paper cites.
Z. Cheng, S. Leng, H. Zhang, Y. Xin, X. Li, G. Chen, Y. Zhu, W. Zhang, Z. Luo, D. Zhao et al. , “Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms,” arXiv preprint arXiv:2406.07476 , 2024
Original
2024
Earlier work this paper cites.
C. Yan, H. Wang, S. Yan, X. Jiang, Y. Hu, G. Kang, W. Xie, and E. Gavves, “Visa: Reasoning video object segmentation via large language models,” in European Conference on Computer Vision . Springer, 2024, pp. 98–115
2024
Earlier work this paper cites.
Y. Zhang, Y. Liu, Z. Guo, Y. Zhang, X. Yang, C. Chen, J. Song, B. Zheng, Y. Yao, Z. Liu et al. , “Llava-uhd v2: an mllm integrating high-resolution feature pyramid via hierarchical window transformer,” arXiv preprint arXiv:2412.13871 , 2024
Original
2024
Earlier work this paper cites.
E. Bigelow, A. Holtzman, H. Tanaka, and T. Ullman, “Forking paths in neural text generation,” arXiv preprint arXiv:2412.07961 , 2024
Original
2024
Earlier work this paper cites.
Z. Lin, T. Liang, J. Xu, X. Wang, R. Luo, C. Shi, S. Li, Y. Yang, and Z. Tu, “Critical tokens matter: Token-level contrastive estimation enhence llm’s reasoning capability,” arXiv preprint arXiv:2411.19943 , 2024
Original
2024
Earlier work this paper cites.
K. Li, Y. Wang, Y. He, Y. Li, Y. Wang, Y. Liu, Z. Wang, J. Xu, G. Chen, P. Luo et al. , “Mvbench: A comprehensive multi-modal video understanding benchmark,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 22 195–22 206
2024
Earlier work this paper cites.

Similar

Y. Liu, S. Li, Y. Liu, Y. Wang, S. Ren, L. Li, S. Chen, X. Sun, and L. Hou, “Tempcompass: Do video llms really understand videos?” arXiv preprint arXiv:2403.00476 , 2024
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2024
Cited alongside, same era.
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 preprint arXiv:2405.21075 , 2024
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Cited alongside, same era.
Y. Li, C. Wang, and J. Jia, “Llama-vid: An image is worth 2 tokens in large language models,” in European Conference on Computer Vision . Springer, 2024, pp. 323–340
2024
Cited alongside, same era.
P. Zhang, K. Zhang, B. Li, G. Zeng, J. Yang, Y. Zhang, Z. Wang, H. Tan, C. Li, and Z. Liu, “Long context transfer from language to vision,” arXiv preprint arXiv:2406.16852 , 2024
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Cited alongside, same era.

Then

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2025

Reinforcing Video Reasoning with Focused Thinking

Dang, Jisheng, Wu, Jingze, Wang, Teng et al.

Understand

Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks.

However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rewarding fails to account for partially correct answers, resulting in high reward variance and inefficient learning.
In this paper, we propose TW-GRPO, a novel framework that enhances visual reasoning with focused thinking and dense reward granularity.
Specifically, we employs a token weighting mechanism that prioritizes tokens with high informational density (estimated by intra-group information entropy), suppressing redundant tokens like generic reasoning prefixes.

Built on

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.
K. Yi, C. Gan, Y. Li, P. Kohli, J. Wu, A. Torralba, and J. B. Tenenbaum, “Clevrer: Collision events for video representation and reasoning,” arXiv preprint arXiv:1910.01442 , 2019
Original
2019
Earlier work this paper cites.
L. von Werra, Y. Belkada, L. Tunstall, E. Beeching, T. Thrush, N. Lambert, S. Huang, K. Rasul, and Q. Gallouédec, “Trl: Transformer reinforcement learning,” https://github.com/huggingface/trl , 2020
2020
Earlier work this paper cites.
R. Zheng, S. Dou, S. Gao, Y. Hua, W. Shen, B. Wang, Y. Liu, S. Jin, Q. Liu, Y. Zhou et al. , “Secrets of rlhf in large language models part i: Ppo,” arXiv preprint arXiv:2307.04964 , 2023
Original
2023
Earlier work this paper cites.
F. Shu, L. Zhang, H. Jiang, and C. Xie, “Audio-visual llm for video understanding,” arXiv preprint arXiv:2312.06720 , 2023
Original
2023
Earlier work this paper cites.
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
Original
2024
Earlier work this paper cites.
J. Xiao, A. Yao, Y. Li, and T. S. Chua, “Can i trust your answer? visually grounded video question answering,” in 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Earlier work this paper cites.
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
Original
2024
Earlier work this paper cites.
Z. Cheng, S. Leng, H. Zhang, Y. Xin, X. Li, G. Chen, Y. Zhu, W. Zhang, Z. Luo, D. Zhao et al. , “Videollama 2: Advancing spatial-temporal modeling and audio understanding in video-llms,” arXiv preprint arXiv:2406.07476 , 2024
Original
2024
Earlier work this paper cites.
C. Yan, H. Wang, S. Yan, X. Jiang, Y. Hu, G. Kang, W. Xie, and E. Gavves, “Visa: Reasoning video object segmentation via large language models,” in European Conference on Computer Vision . Springer, 2024, pp. 98–115
2024
Earlier work this paper cites.
Y. Zhang, Y. Liu, Z. Guo, Y. Zhang, X. Yang, C. Chen, J. Song, B. Zheng, Y. Yao, Z. Liu et al. , “Llava-uhd v2: an mllm integrating high-resolution feature pyramid via hierarchical window transformer,” arXiv preprint arXiv:2412.13871 , 2024
Original
2024
Earlier work this paper cites.
E. Bigelow, A. Holtzman, H. Tanaka, and T. Ullman, “Forking paths in neural text generation,” arXiv preprint arXiv:2412.07961 , 2024
Original
2024
Earlier work this paper cites.
Z. Lin, T. Liang, J. Xu, X. Wang, R. Luo, C. Shi, S. Li, Y. Yang, and Z. Tu, “Critical tokens matter: Token-level contrastive estimation enhence llm’s reasoning capability,” arXiv preprint arXiv:2411.19943 , 2024
Original
2024
Earlier work this paper cites.
K. Li, Y. Wang, Y. He, Y. Li, Y. Wang, Y. Liu, Z. Wang, J. Xu, G. Chen, P. Luo et al. , “Mvbench: A comprehensive multi-modal video understanding benchmark,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 22 195–22 206
2024
Earlier work this paper cites.

Similar

Y. Liu, S. Li, Y. Liu, Y. Wang, S. Ren, L. Li, S. Chen, X. Sun, and L. Hou, “Tempcompass: Do video llms really understand videos?” arXiv preprint arXiv:2403.00476 , 2024
Original
2024
Cited alongside, same era.
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 preprint arXiv:2405.21075 , 2024
Original
2024
Cited alongside, same era.
Y. Li, C. Wang, and J. Jia, “Llama-vid: An image is worth 2 tokens in large language models,” in European Conference on Computer Vision . Springer, 2024, pp. 323–340
2024
Cited alongside, same era.
P. Zhang, K. Zhang, B. Li, G. Zeng, J. Yang, Y. Zhang, Z. Wang, H. Tan, C. Li, and Z. Liu, “Long context transfer from language to vision,” arXiv preprint arXiv:2406.16852 , 2024
Original
2024
Cited alongside, same era.

Then

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

J. Liu, Y. Wang, H. Ma, X. Wu, X. Ma, X. Wei, J. Jiao, E. Wu, and J. Hu, “Kangaroo: A powerful video-language model supporting long-context video input,” arXiv preprint arXiv:2408.15542 , 2024

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B. Wu, S. Yu, Z. Chen, J. B. Tenenbaum, and C. Gan, “Star: A benchmark for situated reasoning in real-world videos,” arXiv preprint arXiv:2405.09711 , 2024

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2024

Cited alongside, same era.

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

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2025

Cited alongside, same era.

L. Chen, L. Li, H. Zhao, Y. Song, and Vinci, “R1-v: Reinforcing super generalization ability in vision-language models with less than $3,” https://github.com/Deep-Agent/R1-V , 2025, accessed: 2025-02-02

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W. Huang, B. Jia, Z. Zhai, S. Cao, Z. Ye, F. Zhao, Z. Xu, Y. Hu, and S. Lin, “Vision-r1: Incentivizing reasoning capability in multimodal large language models,” arXiv preprint arXiv:2503.06749 , 2025

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F. Meng, L. Du, Z. Liu, Z. Zhou, Q. Lu, D. Fu, T. Han, B. Shi, W. Wang, J. He et al. , “Mm-eureka: Exploring the frontiers of multimodal reasoning with rule-based reinforcement learning,” arXiv preprint arXiv:2503.07365 , 2025

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H. Zhou, X. Li, R. Wang, M. Cheng, T. Zhou, and C.-J. Hsieh, “R1-zero’s" aha moment" in visual reasoning on a 2b non-sft model,” arXiv preprint arXiv:2503.05132 , 2025

Original