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

# 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:

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.

{
  "video": "relative/path/to/solution_video.mp4",
  "input_image": "relative/path/to/input_image.png",
  "prompt": "Natural-language task instruction"
}

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}
}