VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding

Hugging Face Daily Papers Papers

Summary

VideoKR introduces a large-scale video reasoning dataset and benchmark designed to enhance knowledge-intensive video understanding through expert-domain content and human-in-the-loop example generation. The dataset contains 315K video reasoning examples over 145K expert-domain videos.

We introduce VideoKR, the first large-scale training corpus specifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315K video reasoning examples over 145K newly collected, CC-licensed, expert-domain videos. We develop a human-in-the-loop, skill-oriented example generation pipeline that targets progressively deeper video reasoning capabilities while ensuring the difficulty, diversity, and reliability of both the examples and their CoT rationales. We also curate VideoKR-Eval, a new expert-annotated benchmark where questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments show that, under a standard SFTrightarrowGRPO pipeline, models post-trained on VideoKR outperform prior post-training approaches on knowledge-intensive video reasoning while remaining competitive on general video reasoning, highlighting data design as a key driver of progress in video reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.
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Source: https://huggingface.co/papers/2606.05259

Abstract

VideoKR presents a large-scale video reasoning dataset and benchmark designed to enhance knowledge-intensive video understanding through expert-domain content and human-in-the-loop example generation.

We introduce VideoKR, the firstlarge-scale training corpusspecifically designed to strengthen knowledge- and reasoning-intensive video understanding. It comprises 315Kvideo reasoningexamples over 145K newly collected, CC-licensed, expert-domain videos. We develop ahuman-in-the-loop,skill-oriented example generationpipeline that targets progressively deepervideo reasoningcapabilities while ensuring the difficulty, diversity, and reliability of both the examples and theirCoT rationales. We also curate VideoKR-Eval, a newexpert-annotated benchmarkwhere questions require genuine video understanding and knowledge-intensive reasoning rather than textual shortcuts. Our experiments show that, under a standard SFTrightarrowGRPO pipeline, models post-trained on VideoKR outperform priorpost-training approacheson knowledge-intensivevideo reasoningwhile remaining competitive on generalvideo reasoning, highlighting data design as a key driver of progress invideo reasoning. We further conduct comprehensive ablations to isolate the contributions of VideoKR, providing actionable insights for future work.

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about 4 hours ago

VideoKR presents a large-scale video reasoning dataset and benchmark designed to enhance knowledge-intensive video understanding through expert-domain content and human-in-the-loop example generation.

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Models citing this paper4

#### minuzero/VideoKR-Qwen3-VL-8B Video-Text-to-Text• 9B• Updatedabout 4 hours ago • 37 • 1 #### minuzero/VideoKR-Qwen2.5-VL-7B-SFT Video-Text-to-Text• 849k• Updatedabout 4 hours ago • 34 #### minuzero/VideoKR-Qwen3-VL-8B-SFT Video-Text-to-Text• 770k• Updatedabout 4 hours ago • 29 #### minuzero/VideoKR-Qwen2.5-VL-7B Video-Text-to-Text• 8B• Updatedabout 4 hours ago • 40

Datasets citing this paper2

#### minuzero/VideoKR-Eval Viewer• Updatedabout 4 hours ago • 2k • 741 #### minuzero/VideoKR-Train Viewer• Updatedabout 4 hours ago • 114k • 118

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