license: apache-2.0
library_name: diffusers
pipeline_tag: image-to-video
base_model:
- Wan-AI/Wan2.2-TI2V-5B
datasets:
- DarthZhu/VideoRLVR-Data
VideoRLVR
VideoRLVR is a reinforcement learning (RL) recipe for training video reasoning models with verifiable rewards, introduced in the paper Video Models Can Reason with Verifiable Rewards.
This checkpoint is an RL-optimized version of Wan2.2-TI2V-5B trained on procedurally generated reasoning tasks including Maze, FlowFree, and Sokoban.
- Paper: Video Models Can Reason with Verifiable Rewards
- Project Page: https://darthzhu.github.io/VideoRLVR-page/
- Repository: https://github.com/luka-group/VideoRLVR
Overview
VideoRLVR formulates video reasoning as the generation of verifiable visual trajectories. It utilizes an SDE-GRPO optimization backbone, dense decomposed rewards, and an Early-Step Focus strategy for efficient training. This approach enables video diffusion models to satisfy explicit spatial, temporal, or logical constraints, moving beyond perceptual imitation toward reliable rule-consistent visual reasoning.
Across tasks like Maze, FlowFree, and Sokoban, VideoRLVR consistently improves over supervised fine-tuning baselines, demonstrating that verifiable RL can effectively optimize models for objective success criteria.
Citation
@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}
}