README.md

---
license: cc-by-nc-4.0
task_categories:
  - image-text-to-video
language:
  - en
size_categories:
  - 10K<n<100K
tags:
  - video-generation
  - video-reasoning
  - reinforcement-learning
  - rlvr
  - verifiable-rewards
  - multimodal
pretty_name: "VideoRLVR-Data: Verifiable Video Reasoning Data"
configs:
  - config_name: default
    data_files:
      - split: train
        path: train/metadata_all.csv
      - split: test
        path: test/metadata_all.csv
  - config_name: maze
    data_files:
      - split: train
        path: train/metadata_maze.csv
      - split: test
        path: test/metadata_maze.csv
  - config_name: flowfree
    data_files:
      - split: train
        path: train/metadata_flowfree.csv
      - split: test
        path: test/metadata_flowfree.csv
  - config_name: sokoban
    data_files:
      - split: train
        path: train/metadata_sokoban.csv
      - split: test
        path: test/metadata_sokoban.csv
---

# VideoRLVR-Data · Verifiable video reasoning data for RLVR

Training and evaluation data accompanying  
**Video Models Can Reason with Verifiable Rewards**.

**VideoRLVR-Data** is built for a stricter question:

> Can a video model generate a full visual trajectory that is not only realistic, but also rule-consistent, executable, and automatically verifiable?

The dataset contains procedurally generated visual reasoning tasks from three domains: Maze, FlowFree, and Sokoban. Each sample pairs an initial visual state and task prompt with a ground-truth solution video. These domains are designed for reinforcement learning with verifiable rewards, where generated videos can be parsed and checked by symbolic rule-based evaluators.

## What's in this repo

| Path | Description |
|---|---|
| `train/` | Training split for VideoRLVR supervised fine-tuning and RL initialization |
| `test/` | Held-out evaluation split generated with disjoint random seeds |
| `train/maze/` | Maze training videos and metadata |
| `train/flowfree/` | FlowFree training videos and metadata |
| `train/sokoban/` | Sokoban training videos and metadata |
| `test/maze/` | Maze test videos and metadata |
| `test/flowfree/` | FlowFree test videos and metadata |
| `test/sokoban/` | Sokoban test videos and metadata |
| `train/metadata_all.csv` | Combined training metadata |
| `test/metadata_all.csv` | Combined test metadata |
| `*/metadata_maze.csv` | Maze-only metadata |
| `*/metadata_flowfree.csv` | FlowFree-only metadata |
| `*/metadata_sokoban.csv` | Sokoban-only metadata |

## Dataset composition

| Domain | Train | Test | Description |
|---|---:|---:|---|
| `maze` | 10,000 | 1,000 | Grid navigation from start to goal while avoiding walls |
| `flowfree` | 10,000 | 1,000 | Connect colored endpoints with non-overlapping paths that fill the grid |
| `sokoban` | 10,000 | 1,000 | Push boxes to target locations under Sokoban transition rules |
| **Total** | **30,000** | **3,000** | Multi-task verifiable video reasoning suite |

Each example contains an input state, a natural-language instruction, and a ground-truth solution video. Videos are rendered at **480×832** resolution with **81 frames**.

## How to use

```bash
# Download the dataset
hf download DarthZhu/VideoRLVR-Data \
  --repo-type dataset \
  --local-dir VideoRLVR-Data

cd VideoRLVR-Data
```

You can load the metadata files directly:
```python
import pandas as pd
from pathlib import Path

root = Path("VideoRLVR-Data")

maze_train = pd.read_csv(root / "train" / "metadata_maze.csv")
flowfree_train = pd.read_csv(root / "train" / "metadata_flowfree.csv")
sokoban_train = pd.read_csv(root / "train" / "metadata_sokoban.csv")
```

# Schema
The metadata files use a simple video-generation format.
```json
{
  "video": "relative/path/to/solution_video.mp4",
  "input_image": "relative/path/to/input_image.png",
  "prompt": "Natural-language task instruction"
}
```

# 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}
}
```