Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918218164076544 |
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| author | Tang, Yolo Y. Shimada, Daiki Hua, Hang Huang, Chao Bi, Jing Feris, Rogerio Xu, Chenliang |
| author_facet | Tang, Yolo Y. Shimada, Daiki Hua, Hang Huang, Chao Bi, Jing Feris, Rogerio Xu, Chenliang |
| contents | Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fixed frames, leading to hallucinations and failures on fine-grained evidence. Inspired by how humans pause, zoom, and re-read critical regions, we introduce Video-R4 (Reinforcing Text-Rich Video Reasoning with Visual Rumination), a video reasoning LMM that performs visual rumination: iteratively selecting frames, zooming into informative regions, re-encoding retrieved pixels, and updating its reasoning state. We construct two datasets with executable rumination trajectories: Video-R4-CoT-17k for supervised practice and Video-R4-RL-30k for reinforcement learning. We propose a multi-stage rumination learning framework that progressively finetunes a 7B LMM to learn atomic and mixing visual operations via SFT and GRPO-based RL. Video-R4-7B achieves state-of-the-art results on M4-ViteVQA and further generalizes to multi-page document QA, slides QA, and generic video QA, demonstrating that iterative rumination is an effective paradigm for pixel-grounded multimodal reasoning. Project Page: https://yunlong10.github.io/Video-R4/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_17490 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination Tang, Yolo Y. Shimada, Daiki Hua, Hang Huang, Chao Bi, Jing Feris, Rogerio Xu, Chenliang Computer Vision and Pattern Recognition Understanding text-rich videos requires reading small, transient textual cues that often demand repeated inspection. Yet most video QA models rely on single-pass perception over fixed frames, leading to hallucinations and failures on fine-grained evidence. Inspired by how humans pause, zoom, and re-read critical regions, we introduce Video-R4 (Reinforcing Text-Rich Video Reasoning with Visual Rumination), a video reasoning LMM that performs visual rumination: iteratively selecting frames, zooming into informative regions, re-encoding retrieved pixels, and updating its reasoning state. We construct two datasets with executable rumination trajectories: Video-R4-CoT-17k for supervised practice and Video-R4-RL-30k for reinforcement learning. We propose a multi-stage rumination learning framework that progressively finetunes a 7B LMM to learn atomic and mixing visual operations via SFT and GRPO-based RL. Video-R4-7B achieves state-of-the-art results on M4-ViteVQA and further generalizes to multi-page document QA, slides QA, and generic video QA, demonstrating that iterative rumination is an effective paradigm for pixel-grounded multimodal reasoning. Project Page: https://yunlong10.github.io/Video-R4/ |
| title | Video-R4: Reinforcing Text-Rich Video Reasoning with Visual Rumination |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.17490 |