---
base_model:
- Qwen/Qwen2.5-7B-Instruct
datasets:
- Video-R1/Video-R1-data
language:
- en
license: apache-2.0
pipeline_tag: video-text-to-text
library_name: transformers
---

# Video-R1: Reinforcing Video Reasoning in MLLMs

This repository contains `Video-R1/Qwen2.5-VL-7B-COT-SFT`, the SFT (Supervised Fine-Tuning) cold start model trained using the Video-R1-COT-165k dataset. This intermediate checkpoint serves as the base model for further RL (Reinforcement Learning) training on the Video-R1-260k dataset to produce the final Video-R1 models.

For more details, please refer to the paper: [Video-R1: Reinforcing Video Reasoning in MLLMs](https://huggingface.co/papers/2503.21776).
The full code and additional resources are available on the [GitHub repository](https://github.com/tulerfeng/Video-R1).

## About Video-R1

Video-R1 represents the first systematic exploration of the R1 paradigm for incentivizing video reasoning within multimodal large language models (MLLMs), inspired by the success of DeepSeek-R1. The project addresses key challenges in video reasoning, particularly the lack of temporal modeling and the scarcity of high-quality video-reasoning data.

To tackle these issues, Video-R1 proposes the T-GRPO algorithm, an extension of GRPO that explicitly encourages models to leverage temporal information in videos for reasoning. It also strategically incorporates high-quality image-reasoning data into the training process. The model was trained on two newly constructed datasets: Video-R1-CoT-165k for SFT cold start and Video-R1-260k for RL training, both comprising image and video data.

Experimental results demonstrate that Video-R1 achieves significant improvements on various video reasoning benchmarks, including VideoMMMU, VSI-Bench, MVBench, and TempCompass. Notably, Video-R1-7B has shown competitive performance, even surpassing proprietary models like GPT-4o on certain video spatial reasoning tasks.



## Sample Usage

We provide a simple generation process for using this SFT cold start model with the `transformers` library.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
from PIL import Image
import cv2
from decord import VideoReader, cpu

# Load model, tokenizer, and processor
model_id = "Video-R1/Qwen2.5-VL-7B-COT-SFT" # This specific SFT checkpoint
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Function to load video frames
def load_video_frames(video_path, num_frames=16):
    vr = VideoReader(video_path, ctx=cpu(0))
    total_frames = len(vr)
    indices = [int(i * (total_frames / num_frames)) for i in range(num_frames)]
    frames = vr.get_batch(indices).asnumpy()
    frames = [Image.fromarray(frame) for frame in frames]
    return frames

# Example usage
# Replace with your actual video path
# For demonstration, ensure a video file like 'examples/video1.mp4' exists or adjust path
video_path = "./examples/video1.mp4" 
frames = load_video_frames(video_path)
text = "Describe this video in detail."

# Prepare inputs
inputs = processor(frames=frames, text=text, return_tensors="pt").to("cuda")

# Generate response
output = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Citation

If you find our work helpful for your research, please consider citing our work:

```bibtex
@article{feng2025video,
  title={Video-R1: Reinforcing Video Reasoning in MLLMs},
  author={Feng, Kaituo and Gong, Kaixiong and Li, Bohao and Guo, Zonghao and Wang, Yibing and Peng, Tianshuo and Wang, Benyou and Yue, Xiangyu},
  journal={arXiv preprint arXiv:2503.21776},
  year={2025}
}
```