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TinyLLaVA-Video-R1

Video-Text-to-Text
Transformers
TensorBoard
Safetensors
tinyllava
text-generation
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xet
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  **<center><span style="font-size:2em;">TinyLLaVA-Video-R1</span></center>**
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- [![arXiv](https://img.shields.io/badge/Arxiv-2402.14289-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2504.09641)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/ZhangXJ199/TinyLLaVA-Video-R1)
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  Here, we introduce a small-scale video reasoning model TinyLLaVA-Video-R1, based on the traceably trained model [TinyLLaVA-Video](https://github.com/ZhangXJ199/TinyLLaVA-Video). After reinforcement learning on general Video-QA datasets, the model not only significantly improves its reasoning and thinking abilities, but also exhibits the emergent characteristic of “aha moments”.
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  **<center><span style="font-size:2em;">TinyLLaVA-Video-R1</span></center>**
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+ [![arXiv](https://img.shields.io/badge/Arxiv-2504.09641-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2504.09641)[![Github](https://img.shields.io/badge/Github-Github-blue.svg)](https://github.com/ZhangXJ199/TinyLLaVA-Video-R1)
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  Here, we introduce a small-scale video reasoning model TinyLLaVA-Video-R1, based on the traceably trained model [TinyLLaVA-Video](https://github.com/ZhangXJ199/TinyLLaVA-Video). After reinforcement learning on general Video-QA datasets, the model not only significantly improves its reasoning and thinking abilities, but also exhibits the emergent characteristic of “aha moments”.
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