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minuzero
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Qwen/Qwen2.5-VL-7B-Instruct
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This repository contains the VideoKR-Qwen2.5-VL-7B-SFT model presented in VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding (ICML 2026 Spotlight).
VideoKR-Qwen2.5-VL-7B-SFT is obtained by supervised fine-tuning Qwen2.5-VL-7B-Instruct on VideoKR-SFT-201K for one epoch. Each training example includes a high-quality chain-of-thought (CoT) rationale as the supervision target. This SFT checkpoint serves as the starting point for subsequent GRPO reinforcement learning, yielding the final VideoKR-Qwen2.5-VL-7B model.
| Resource | Link |
|---|---|
| Training data | minuzero/VideoKR-Train |
| Evaluation data | minuzero/VideoKR-Eval |
| GRPO checkpoint (Qwen2.5-VL) | minuzero/VideoKR-Qwen2.5-VL-7B |
| SFT checkpoint (Qwen3-VL) | minuzero/VideoKR-Qwen3-VL-8B-SFT |
| GRPO checkpoint (Qwen3-VL) | minuzero/VideoKR-Qwen3-VL-8B |
For detailed training instructions, please refer to the GitHub repository.
bash
cd /path/to/VideoKR/llamafactoryconda activate videokr_train # Prepare SFT datamkdir -p data/rawhuggingface-cli download minuzero/VideoKR-Train \ --repo-type dataset --local-dir data/raw \ --include "VideoKR-COT-201K.jsonl" python local_script/prepare_videokr_sft_data.py \ --input data/raw/VideoKR-COT-201K.jsonl \ --output data/videokr_train.json # Launch SFTbash local_script/train_videokr.sh qwen2_5vlIf you find VideoKR useful in your research, please cite our paper:
text
@misc{fu2026videokrknowledgereasoningintensivevideo, title={VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding}, author={Lin Fu and Zheyuan Yang and Yang Wang and Tingyu Song and Arman Cohan and Yilun Zhao}, year={2026}, eprint={2606.05259}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2606.05259}, }cd /path/to/VideoKR/llamafactoryconda activate videokr_train # Prepare SFT datamkdir -p data/rawhuggingface-cli download minuzero/VideoKR-Train \ --repo-type dataset --local-dir data/raw \ --include "VideoKR-COT-201K.jsonl" python local_script/prepare_videokr_sft_data.py \ --input data/raw/VideoKR-COT-201K.jsonl \ --output data/videokr_train.json # Launch SFTbash local_script/train_videokr.sh qwen2_5vl@misc{fu2026videokrknowledgereasoningintensivevideo, title={VideoKR: Towards Knowledge- and Reasoning-Intensive Video Understanding}, author={Lin Fu and Zheyuan Yang and Yang Wang and Tingyu Song and Arman Cohan and Yilun Zhao}, year={2026}, eprint={2606.05259}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2606.05259}, }