Datasets:

DarthZhu
/

VideoRLVR-Data

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

# 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}
}