In this episode, we discuss
TinyLLaVA-Video-R1: Towards Smaller LMMs for Video Reasoning by Xingjian Zhang, Siwei Wen, Wenjun Wu, Lei Huang. The paper introduces TinyLLaVA-Video-R1, a small-scale video reasoning model with no more than 4 billion parameters, designed to enhance reasoning abilities using reinforcement learning on general Video-QA datasets. Unlike previous studies that focus on large models and specialized datasets, this work demonstrates significant improvements in reasoning and the emergence of "aha moments" in a more computationally accessible model. The authors also provide experimental insights to guide future research in developing video reasoning capabilities for smaller models.
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