---
license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-video
base_model:
- DarthZhu/VideoRLVR-Wan2.2-Base
datasets:
- DarthZhu/VideoRLVR-Data
---

# VideoRLVR

VideoRLVR is a reinforcement learning (RL) recipe for training video reasoning models with verifiable rewards. This model is a reinforcement-learning optimized version of [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B), presented in the paper [Video Models Can Reason with Verifiable Rewards](https://huggingface.co/papers/2605.15458).

The model uses an SDE-GRPO optimization backbone and rule-based feedback to improve visual reasoning in complex, procedurally generated tasks such as Maze, FlowFree, and Sokoban.

- **Paper:** [Video Models Can Reason with Verifiable Rewards](https://huggingface.co/papers/2605.15458)
- **Project Page:** [https://darthzhu.github.io/VideoRLVR-page/](https://darthzhu.github.io/VideoRLVR-page/)
- **Code:** [https://github.com/luka-group/VideoRLVR](https://github.com/luka-group/VideoRLVR)

## Method Overview

VideoRLVR formulates video reasoning as the generation of verifiable visual trajectories. Key components include:
1. **SDE-GRPO**: An optimization backbone for video diffusion models.
2. **Dense Decomposed Rewards**: Verifiable, rule-based feedback to guide the model.
3. **Early-Step Focus**: A strategy that restricts policy optimization to the early denoising phase, significantly reducing training latency while preserving performance.

## Citation

```bibtex
@article{zhu2026video,
  title={Video Models Can Reason with Verifiable Rewards}, 
  author={Tinghui Zhu and Sheng Zhang and James Y. Huang and Selena Song and Xiaofei Wen and Yuankai Li and Hoifung Poon and Muhao Chen},
  journal={arXiv preprint arXiv:2605.15458},
  year={2026}
}
```