Datasets:

Video-Reason
/

VBVR-MultiStep

---
license: cc-by-4.0
task_categories:
  - image-to-video
  - text-to-video
language:
  - en
size_categories:
  - 100K<n<1M
pretty_name: VBVR-MultiStep
tags:
  - video-reasoning
  - multi-step
  - long-horizon
  - image-to-video
  - training
---
# VBVR-MultiStep

The **~360k-sample programmatic training corpus** for long-horizon multi-step image-to-video (I2V) reasoning. Companion to the frozen [VBVR-MultiStep-Bench](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep-Bench) (180-instance evaluation split).

Part of the **VBVR (Very Big Video Reasoning Suite)** project: <https://video-reason.com>. See [Wang et al., ICML 2026](https://icml.cc/virtual/2026/poster/65709) for the parent suite.

## At a glance

| Property | Value |
|---|---|
| Tasks | **36** parameterized tasks (`Multi-01` … `Multi-36`) |
| Reasoning families | Navigation, Planning, CSP, Execution, Geometry, Physics |
| Total samples | **~360,000** (≈10k per task) |
| Total size | **~164 GB** |
| Format | Tar.gz shards (nested per-sample folders) + Parquet metadata |
| Shards | 7,200 (≈50 samples per shard) |
| License | CC-BY-4.0 |

## Repository layout

```
.
├── README.md
├── croissant.json                       # Croissant + RAI metadata
├── data/
│   ├── metadata.parquet                 # global index of all 360k samples
│   └── metadata_shards/
│       └── Multi-XX_<name>.parquet      # per-task metadata (36 files)
├── questions/                           # WebDataset shards
│   └── Multi-XX_<name>_NNNNN-NNNNN.tar.gz
│       └── (50 samples per shard, 5 files per sample, see "Sample format" below)
└── sample/                              # ~5 GB representative subset for quick inspection
    ├── data/metadata_shards/...
    └── questions/                        # 6 shards × 36 tasks = 216 shards
```

The `sample/` subdirectory is a 5 GB pre-curated subset (the first 300 samples of every task) for reviewers and quick experimentation. To pull it:

```bash
huggingface-cli download Video-Reason/VBVR-MultiStep \
    --repo-type dataset \
    --include "sample/**" \
    --local-dir ./vbvr-multistep-sample
```

## Sample format (inside each `.tar.gz` shard)

Each shard expands to a nested folder tree, identical in shape to the evaluation split:

```
Multi-XX_<name>_data-generator/
└── Multi-XX_<name>_data-generator_task/
    └── Multi-XX_<name>_data-generator_<id>/
        ├── first_frame.png         # conditioning frame
        ├── prompt.txt              # natural-language task contract
        ├── final_frame.png         # target endpoint (held-out at inference)
        ├── ground_truth.mp4        # reference rollout
        └── question_metadata.json  # seed, version, tolerances, task fields
```

Each shard contains 50 such instance folders. The five-artifact contract is identical to the evaluation split.

To extract:

```bash
tar xzf Multi-01_maze_shortest_path_data-generator_00000-00049.tar.gz
```

## Loading

### Per-task metadata (recommended entry point)

```python
import pandas as pd
m = pd.read_parquet(
    "hf://datasets/Video-Reason/VBVR-MultiStep/data/metadata_shards/Multi-01_maze_shortest_path_data-generator.parquet"
)
print(m.head())
```

### Direct shard download

```python
from huggingface_hub import hf_hub_download
import tarfile
shard = hf_hub_download(
    "Video-Reason/VBVR-MultiStep",
    "questions/Multi-01_maze_shortest_path_data-generator_00000-00049.tar.gz",
    repo_type="dataset",
)
with tarfile.open(shard) as t:
    t.extractall("./extracted")
```

### Pull only the 5 GB sample

```bash
huggingface-cli download Video-Reason/VBVR-MultiStep \
    --repo-type dataset \
    --include "sample/**" \
    --local-dir ./vbvr-multistep-sample
```

## Splits and seeds

The training corpus is partitioned into disjoint seed bands:

| Band | Seed range | Samples per task | Total samples |
|---|---|---|---|
| First-half | 1–5,000 | 5,000 | ~170k (across 34 trained tasks) |
| Second-half | 5,001–10,000 | 5,000 | ~170k (across 34 trained tasks) |

Both bands are disjoint from the **180-instance evaluation seeds** in `VBVR-MultiStep-Bench`. The submitted paper trains on 34 of 36 tasks; the released corpus contains all 36 task families.

## Reasoning families

See the [bench dataset card](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep-Bench) for the family taxonomy. Each family contributes 6 tasks, for 36 total.

## Intended use and out-of-scope

- **Primary use**: training I2V systems on long-horizon multi-step reasoning under explicit per-step rules.
- **Out-of-scope**: this corpus is fully synthetic and stylized; transfer to unconstrained open-world video is not validated by this release.
- **Not validated for**: production VLM pretraining at scale, real-world video generation, or any safety-critical use.

## License

Released under **CC-BY-4.0**. Generators consume only released task definitions; no third-party copyrighted content is embedded.

Derivatives of `Wan2.2-I2V-A14B` (Apache-2.0) referenced in the companion paper comply with the upstream license. This dataset does not redistribute model weights.

## Responsible AI

The dataset is fully synthetic. There are no human subjects, no scraped media, and no personal or sensitive information. Known biases inherit from the deterministic generators — every task family covers a deliberately narrow conceptual slice, and visual style is controlled by a fixed renderer family (no demographic content). See [`croissant.json`](./croissant.json) for the complete RAI metadata.

	---
	license: cc-by-4.0
	task_categories:
	- image-to-video
	- text-to-video
	language:
	- en
	size_categories:
	- 100K<n<1M
	pretty_name: VBVR-MultiStep
	tags:
	- video-reasoning
	- multi-step
	- long-horizon
	- image-to-video
	- training
	---

	# VBVR-MultiStep

	The ~360k-sample programmatic training corpus for long-horizon multi-step image-to-video (I2V) reasoning. Companion to the frozen [VBVR-MultiStep-Bench](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep-Bench) (180-instance evaluation split).

	Part of the VBVR (Very Big Video Reasoning Suite) project: <https://video-reason.com>. See [Wang et al., ICML 2026](https://icml.cc/virtual/2026/poster/65709) for the parent suite.

	## At a glance

	\| Property \| Value \|
	\|---\|---\|
	\| Tasks \| 36 parameterized tasks (`Multi-01` … `Multi-36`) \|
	\| Reasoning families \| Navigation, Planning, CSP, Execution, Geometry, Physics \|
	\| Total samples \| ~360,000 (≈10k per task) \|
	\| Total size \| ~164 GB \|
	\| Format \| Tar.gz shards (nested per-sample folders) + Parquet metadata \|
	\| Shards \| 7,200 (≈50 samples per shard) \|
	\| License \| CC-BY-4.0 \|

	## Repository layout

	```
	.
	├── README.md
	├── croissant.json # Croissant + RAI metadata
	├── data/
	│ ├── metadata.parquet # global index of all 360k samples
	│ └── metadata_shards/
	│ └── Multi-XX_<name>.parquet # per-task metadata (36 files)
	├── questions/ # WebDataset shards
	│ └── Multi-XX_<name>_NNNNN-NNNNN.tar.gz
	│ └── (50 samples per shard, 5 files per sample, see "Sample format" below)
	└── sample/ # ~5 GB representative subset for quick inspection
	├── data/metadata_shards/...
	└── questions/ # 6 shards × 36 tasks = 216 shards
	```

	The `sample/` subdirectory is a 5 GB pre-curated subset (the first 300 samples of every task) for reviewers and quick experimentation. To pull it:

	```bash
	huggingface-cli download Video-Reason/VBVR-MultiStep \
	--repo-type dataset \
	--include "sample/**" \
	--local-dir ./vbvr-multistep-sample
	```

	## Sample format (inside each `.tar.gz` shard)

	Each shard expands to a nested folder tree, identical in shape to the evaluation split:

	```
	Multi-XX_<name>_data-generator/
	└── Multi-XX_<name>_data-generator_task/
	└── Multi-XX_<name>_data-generator_<id>/
	├── first_frame.png # conditioning frame
	├── prompt.txt # natural-language task contract
	├── final_frame.png # target endpoint (held-out at inference)
	├── ground_truth.mp4 # reference rollout
	└── question_metadata.json # seed, version, tolerances, task fields
	```

	Each shard contains 50 such instance folders. The five-artifact contract is identical to the evaluation split.

	To extract:

	```bash
	tar xzf Multi-01_maze_shortest_path_data-generator_00000-00049.tar.gz
	```

	## Loading

	### Per-task metadata (recommended entry point)

	```python
	import pandas as pd
	m = pd.read_parquet(
	"hf://datasets/Video-Reason/VBVR-MultiStep/data/metadata_shards/Multi-01_maze_shortest_path_data-generator.parquet"
	)
	print(m.head())
	```

	### Direct shard download

	```python
	from huggingface_hub import hf_hub_download
	import tarfile
	shard = hf_hub_download(
	"Video-Reason/VBVR-MultiStep",
	"questions/Multi-01_maze_shortest_path_data-generator_00000-00049.tar.gz",
	repo_type="dataset",
	)
	with tarfile.open(shard) as t:
	t.extractall("./extracted")
	```

	### Pull only the 5 GB sample

	```bash
	huggingface-cli download Video-Reason/VBVR-MultiStep \
	--repo-type dataset \
	--include "sample/**" \
	--local-dir ./vbvr-multistep-sample
	```

	## Splits and seeds

	The training corpus is partitioned into disjoint seed bands:

	\| Band \| Seed range \| Samples per task \| Total samples \|
	\|---\|---\|---\|---\|
	\| First-half \| 1–5,000 \| 5,000 \| ~170k (across 34 trained tasks) \|
	\| Second-half \| 5,001–10,000 \| 5,000 \| ~170k (across 34 trained tasks) \|

	Both bands are disjoint from the 180-instance evaluation seeds in `VBVR-MultiStep-Bench`. The submitted paper trains on 34 of 36 tasks; the released corpus contains all 36 task families.

	## Reasoning families

	See the [bench dataset card](https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep-Bench) for the family taxonomy. Each family contributes 6 tasks, for 36 total.

	## Intended use and out-of-scope

	- Primary use: training I2V systems on long-horizon multi-step reasoning under explicit per-step rules.
	- Out-of-scope: this corpus is fully synthetic and stylized; transfer to unconstrained open-world video is not validated by this release.
	- Not validated for: production VLM pretraining at scale, real-world video generation, or any safety-critical use.

	## License

	Released under CC-BY-4.0. Generators consume only released task definitions; no third-party copyrighted content is embedded.

	Derivatives of `Wan2.2-I2V-A14B` (Apache-2.0) referenced in the companion paper comply with the upstream license. This dataset does not redistribute model weights.

	## Responsible AI

	The dataset is fully synthetic. There are no human subjects, no scraped media, and no personal or sensitive information. Known biases inherit from the deterministic generators — every task family covers a deliberately narrow conceptual slice, and visual style is controlled by a fixed renderer family (no demographic content). See [`croissant.json`](./croissant.json) for the complete RAI metadata.