Improve model card and metadata (#1)

- Improve model card and metadata (772d72ff63760e3cba8b16ae19374bc2c760d61e)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,6 +1,36 @@
 ---
-datasets:
-- DarthZhu/VideoRLVR-Data
+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](https://huggingface.co/papers/2605.15458). 
+
+This checkpoint is an RL-optimized version of [Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) trained on procedurally generated reasoning tasks including 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/)
+- **Repository:** [https://github.com/luka-group/VideoRLVR](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
+
+```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}
+}
+```