🌍 World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning

📄 Paper • 💻 Code • 🤗 Dataset

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✨ Overview

This repository provides a full dataset for the paper:

World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning
Wanyue Zhang et al., 2026

🔍 Motivation
Vision-Language Models (VLMs) excel at static visual understanding but struggle with dynamic spatial reasoning, such as predicting how a scene changes under actions (e.g., moving forward, turning).

💡 Key Idea
We introduce World2VLM, a framework that uses world models as training-time teachers to distill spatial imagination into VLMs—enabling them to reason about future views and action consequences without external simulation at inference time.

📦 This repository contains:

• A compact demo dataset showcasing the data construction pipeline

• Representative trajectory-based supervision samples

• Examples of 8 dynamic spatial reasoning task types

• Full Hugging Face release of the World2VLM dataset. It is organized into two dataset configs:

• grpo: a small, balanced set for policy optimization / alignment-style training

• sft: the large merged supervised fine-tuning set

Both configs are stored as Parquet shards with embedded image features, so the dataset can be loaded directly from the Hub without a separate image directory.

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🧠 What is World2VLM?

World2VLM trains VLMs to mentally simulate the world by learning from world-model-generated transitions:

• Input: an image + an action (e.g., move forward)
• World model: generates the future view
• Output: structured supervision for reasoning

This enables two key capabilities:

• 🔁 Inverse reasoning: infer the action from image changes
• 🔮 Forward reasoning: predict what happens after an action

Unlike prior work, no world model is needed at inference time.

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📂 Dataset Structure

data/
├── grpo/
│   ├── train-00000.parquet
│   └── train-00001.parquet
└── sft/
    ├── train-00000.parquet
    ├── train-00001.parquet
    └── ...

data-demo/
├── SVC-RealScene-demo
│   ├── tasks_demo.jsonl
│   └── scenes/demo_scene/...
├── SVC-SimulatedScene-demo
│   ├── tasks_demo.jsonl
│   └── scenes/demo_scene/...
├── HY-WorldPlay-RealScene-demo
│   ├── tasks_demo.jsonl
│   └── scenes/demo_scene/...
└── HY-WorldPlay-SimulatedScene-demo
    ├── tasks_demo.jsonl
    └── scenes/demo_scene/...

The release also includes:

• README.md
• build_summary.json

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🧾 Data Format

Each row is a two-turn conversation with multimodal inputs:

• task_type: one of A1, A2, A3, A4, D1, D2, D3, D4
• messages: chat turns containing a user prompt and an assistant target
• images: embedded images referenced by the prompt
• num_images: number of images in the row
• source_domain: scannet or MulSeT
• scene_id: source scene identifier
• traj_name: trajectory name
• source_file: original JSONL source file
• source_row_idx: row index inside the source file

Example shape:

{
  "task_type": "A1",
  "messages": [
    {
      "role": "user",
      "content": "<image><image>How many meters did the camera move to get the second image? Answer as: move forward X meters."
    },
    {
      "role": "assistant",
      "content": "move forward 4.4 meters"
    }
  ],
  "images": [...]
}

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⏩ Loading The Dataset

from datasets import load_dataset

grpo = load_dataset("WanyueZhang/World2VLM", "grpo", split="train")
sft = load_dataset("WanyueZhang/World2VLM", "sft", split="train")

sample = grpo[0]
print(sample["task_type"])
print(sample["messages"][0]["content"])
print(sample["images"][0])

The images column is decoded as image objects by datasets.

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🔍 What Is Included

Config	Rows	Shards	Source domains	Unique image references
grpo	2,000	2	1,000 ScanNet + 1,000 MulSet	3,600
sft	103,097	104	66,174 ScanNet + 36,923 MulSet	115,164

Additional notes:

• The GRPO split is balanced across all 8 task types.
• The SFT split merges the full supervised corpus used for fine-tuning.
• Image data is embedded directly into the Parquet files.
• Each row contains one or two images depending on the task.

We also provide four compact subsets covering:

Teacher Model	Scene Type	Description
SVC	Real Scene	Camera-conditioned view synthesis
SVC	Simulated Scene	Synthetic environment transitions
HY-WorldPlay	Real Scene	Action-conditioned world dynamics
HY-WorldPlay	Simulated Scene	Long-horizon simulated trajectories

Each subset includes:

• 🎬 A trajectory bundle (images + metadata)
• 📝 A tasks_demo.jsonl file with structured supervision

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🧩 Task Suite (8 Types)

World2VLM defines a bidirectional task suite:

🔁 Motion-Centric (A-series)

Task	Description	Typical answer format
A1	Motion distance estimation	move forward X meters / move left X meters
A2	Motion orientation estimation	turn left X degrees / turn right X degrees
A3	Multi-step motion prediction	action1; action2; action3
A4	Action-sequence verification	true/false or A/B

🎯 Object-Centric (D-series)

Task	Description	Typical answer format
D1	Post-action bounding box prediction	bbox [x1, y1, x2, y2]
D2	Post-action visibility detection	yes/no or A/B
D3	Cross-view action inference	action1; action2
D4	Object consistency across views	yes/no or A/B

💡 These tasks jointly enforce:

• Understanding camera motion
• Tracking object transformations
• Reasoning about viewpoint changes

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⚙️ Data Construction Pipeline

The dataset is generated using the World2VLM pipeline: use world-model-style transition supervision to teach a VLM to reason over changes between views.

1. 🖼️ Start from an anchor image
2. 🎮 Sample an egocentric action sequence
3. 🌐 Generate future views via world models
4. 🧠 Convert transitions into:
   • Forward tasks (predict outcomes)
   • Inverse tasks (recover actions)

This yields structured supervision of the form:

• P(action | before, after) (inverse)
• P(outcome | before, action) (forward)

The GRPO split is a balanced subset constructed from the complete part1 + part2 source shards. The SFT split is the merged supervised corpus used for fine-tuning.

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🚀 Key Features

• 🧠 Spatial imagination distilled into VLMs
• 🔄 Bidirectional reasoning supervision
• 🧩 Multi-task structured dataset
• ⚡ No world model needed at inference
• 🌍 Supports both real and simulated scenes

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🏆 Why This Matters

World2VLM addresses a core limitation:

❌ VLMs fail at mental simulation
✅ World models can simulate—but are expensive

👉 Our solution:
Train VLMs to internalize world-model reasoning.

This leads to:

• Better dynamic spatial reasoning
• Lower inference cost
• Improved performance on spatial reasoning benchmarks

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📝 Notes

• This is the full release, not the earlier compact demo-only layout.
• The dataset is packaged for direct use with Hugging Face datasets.
• Please confirm upstream redistribution terms for the source scene data before using the dataset in downstream releases.

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📚 Citation

If you find this work useful, please cite:

@misc{zhang2026world2vlmdistillingworldmodel,
      title={World2VLM: Distilling World Model Imagination into VLMs for Dynamic Spatial Reasoning}, 
      author={Wanyue Zhang and Wenxiang Wu and Wang Xu and Jiaxin Luo and Helu Zhi and Yibin Huang and Shuo Ren and Zitao Liu and Jiajun Zhang},
      year={2026},
      eprint={2604.26934},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.26934}, 
}

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🤝 Acknowledgements

We thank the community for advances in:

• 🌐 World Models (e.g., SVC(https://arxiv.org/abs/2503.14489), HY-WorldPlay(https://arxiv.org/abs/2412.03603))
• 🤖 Vision-Language Models
• 🧠 Spatial reasoning benchmarks

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📬 Contact

For questions or collaborations, please open an issue or contact the authors via the paper.

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