VisGym/inference-dataset

VLM-Gym Inference Dataset

This dataset contains pre-defined test episodes and initial states for evaluating Vision-Language Models (VLMs) on the VLM-Gym benchmark.

Dataset Structure

inference-dataset/
├── test_set_easy/           # Easy difficulty test episodes (JSONL)
├── test_set_hard/           # Hard difficulty test episodes (JSONL)
├── initial_states_easy/     # Initial environment states for easy episodes (JSON)
├── initial_states_hard/     # Initial environment states for hard episodes (JSON)
└── partial_datasets/        # Assets required by some environments
    ├── objaverse/           # 3D models for mental rotation tasks
    ├── counting/            # Images for counting tasks
    ├── refcoco+/            # Images for referring expression tasks
    └── ...

Tasks Included

Task	Description
maze_2d	2D maze navigation
maze_3d	3D maze navigation
mental_rotation_2d	2D shape rotation matching
mental_rotation_3d_cube	3D cube rotation matching
mental_rotation_3d_objaverse	3D object rotation matching
jigsaw	Jigsaw puzzle solving
sliding_block	Sliding block puzzle
colorization	Image colorization
counting	Object counting
patch_reassembly	Image patch reassembly
matchstick_equation	Matchstick equation solving
matchstick_rotation	Matchstick rotation
video_unshuffle	Video frame ordering
zoom_in_puzzle	Zoom-in puzzle solving
fetch_reach	Robotic reaching (easy only)
fetch_pick_and_place	Robotic manipulation (hard only)
referring_dot_pointing	Referring expression grounding (easy only)

Quick Start

Installation

pip install huggingface_hub

Download Full Dataset

from huggingface_hub import snapshot_download

dataset_path = snapshot_download(
    repo_id="VisGym/inference-dataset",
    repo_type="dataset",
)

Download Specific Subsets

from huggingface_hub import snapshot_download

# Download only test sets (small, no large assets)
dataset_path = snapshot_download(
    repo_id="VisGym/inference-dataset",
    repo_type="dataset",
    allow_patterns=["test_set_easy/**", "test_set_hard/**"],
)

# Download only easy difficulty
dataset_path = snapshot_download(
    repo_id="VisGym/inference-dataset",
    repo_type="dataset",
    allow_patterns=["*_easy/**"],
)

Using the Loader Script

# Download everything
python load_from_hf.py --output_dir ./inference_dataset

# Download only test sets (no large assets)
python load_from_hf.py --output_dir ./inference_dataset --subset test_sets

# Download only easy difficulty
python load_from_hf.py --output_dir ./inference_dataset --subset easy

File Formats

Test Set Files (JSONL)

Each line in the JSONL files contains an episode specification:

{"seed": 1803372, "env_id": "maze_2d/hard", "episode_seed": 1052368083, "extra_state": null}

Initial State Files (JSON)

JSON files containing the initial state for reproducible episode starts:

{
  "object_path": "000-156/fa3dad5169784cec85b96682231e3f44.glb",
  "secret_yaw": 1.098,
  "secret_pitch": 0.487,
  ...
}

Usage with VLM-Gym

from pathlib import Path
import json

# Load test episodes
test_file = Path(dataset_path) / "test_set_easy" / "maze_2d__easy" / "*.jsonl"
for jsonl_file in test_file.parent.glob("*.jsonl"):
    with open(jsonl_file) as f:
        for line in f:
            episode = json.loads(line)
            env_id = episode["env_id"]
            seed = episode["seed"]
            episode_seed = episode["episode_seed"]
            # Use with VLM-Gym inference runner

Citation

If you use this dataset, please cite:

@misc{wang2026visgymdiversecustomizablescalable,
      title={VisGym: Diverse, Customizable, Scalable Environments for Multimodal Agents}, 
      author={Zirui Wang and Junyi Zhang and Jiaxin Ge and Long Lian and Letian Fu and Lisa Dunlap and Ken Goldberg and XuDong Wang and Ion Stoica and David M. Chan and Sewon Min and Joseph E. Gonzalez},
      year={2026},
      eprint={2601.16973},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.16973}, 
}

License

MIT License