Video Finetuning Improves Reasoning Between Frames

Ruiqi Yang; Tian Yun; Zihan Wang; Ellie Pavlick

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arXiv · 2511.12868

Video Finetuning Improves Reasoning Between Frames

Ruiqi Yang·Tian Yun·Zihan Wang·Ellie Pavlick

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Abstract

Multimodal large language models (LLMs) have made rapid progress in visual understanding, yet their extension from images to videos often reduces to a naive concatenation of frame tokens. In this work, we investigate what video finetuning brings to multimodal LLMs. We propose Visual Chain-of-Thought (vCoT), an explicit reasoning process that generates transitional event descriptions between consecutive frames. Using vCoT, we systematically compare image-only LVLMs with their video-finetuned counterparts, both with and without access to these transitional cues. Our experiments show that vCoT significantly improves the performance of image-only models on long-form video question answering, while yielding only marginal gains for video-finetuned models. This suggests that the latter already capture frame-to-frame transitions implicitly. Moreover, we find that video models transfer this temporal reasoning ability to purely static settings, outperforming image models' baselines on relational visual reasoning tasks.

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BibTeX RIS

Ruiqi Yang, Tian Yun, Zihan Wang, Ellie Pavlick. 2025-11-17. Video Finetuning Improves Reasoning Between Frames. https://arxiv.org/abs/2511.12868

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