Initial release: README + Croissant + 5GB representative sample (216 shards, 36 tasks)

README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - video-text-to-text
+language:
+  - en
+size_categories:
+  - 100K<n<1M
+pretty_name: VBVR-MultiStep
+tags:
+  - video-reasoning
+  - multi-step
+  - long-horizon
+  - image-to-video
+  - training
+  - webdataset
+---
+
+# 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.
+
+## Citation
+
+```bibtex
+@inproceedings{vbvr_multistep_2026,
+  title={Could Video Generation Models Solve Long-Horizon Multi-Step Reasoning Tasks?},
+  author={Anonymous},
+  booktitle={NeurIPS Datasets and Benchmarks},
+  year={2026},
+  note={Under review.}
+}
+
+@inproceedings{wang2026vbvr,
+  title={A Very Big Video Reasoning Suite},
+  author={Wang, Maijunxian and others},
+  booktitle={ICML},
+  year={2026},
+  url={https://icml.cc/virtual/2026/poster/65709}
+}
+```

croissant.json

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+{
+  "@context": {
+    "@language": "en",
+    "@vocab": "https://schema.org/",
+    "citeAs": "cr:citeAs",
+    "column": "cr:column",
+    "conformsTo": "dct:conformsTo",
+    "cr": "http://mlcommons.org/croissant/",
+    "rai": "http://mlcommons.org/croissant/RAI/",
+    "data": {"@id": "cr:data", "@type": "@json"},
+    "dataType": {"@id": "cr:dataType", "@type": "@vocab"},
+    "dct": "http://purl.org/dc/terms/",
+    "examples": {"@id": "cr:examples", "@type": "@json"},
+    "extract": "cr:extract",
+    "field": "cr:field",
+    "fileProperty": "cr:fileProperty",
+    "fileObject": "cr:fileObject",
+    "fileSet": "cr:fileSet",
+    "format": "cr:format",
+    "includes": "cr:includes",
+    "isLiveDataset": "cr:isLiveDataset",
+    "jsonPath": "cr:jsonPath",
+    "key": "cr:key",
+    "md5": "cr:md5",
+    "parentField": "cr:parentField",
+    "path": "cr:path",
+    "recordSet": "cr:recordSet",
+    "references": "cr:references",
+    "regex": "cr:regex",
+    "repeated": "cr:repeated",
+    "replace": "cr:replace",
+    "sc": "https://schema.org/",
+    "separator": "cr:separator",
+    "source": "cr:source",
+    "subField": "cr:subField",
+    "transform": "cr:transform"
+  },
+  "@type": "sc:Dataset",
+  "name": "VBVR-MultiStep",
+  "conformsTo": "http://mlcommons.org/croissant/1.0",
+  "description": "The ~360,000-sample programmatic training corpus for long-horizon multi-step image-to-video reasoning. 36 parameterized tasks across six reasoning families (Navigation, Planning, CSP, Execution, Geometry, Physics). Distributed as 7,200 tar.gz shards (≈50 samples per shard) plus Parquet metadata; each instance follows a five-artifact contract identical to the VBVR-MultiStep-Bench evaluation split.",
+  "alternateName": ["VBVR-MultiStep Training Corpus"],
+  "creator": {
+    "@type": "sc:Organization",
+    "name": "Video-Reason",
+    "url": "https://video-reason.com"
+  },
+  "datePublished": "2026-05-06",
+  "keywords": ["video reasoning", "multi-step reasoning", "long-horizon", "image-to-video", "training", "synthetic", "tar.gz", "parquet"],
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "url": "https://huggingface.co/datasets/Video-Reason/VBVR-MultiStep",
+  "version": "1.0.0",
+  "isLiveDataset": false,
+  "rai:dataCollection": "Fully synthetic. Each of the 36 tasks ships a deterministic generator that emits the five-artifact contract (first_frame.png, prompt.txt, final_frame.png, ground_truth.mp4, question_metadata.json) from a (task, seed) pair. No scraping, no human subjects, no third-party media, no manual annotation. The training corpus is partitioned into disjoint seed bands (1–5,000 and 5,001–10,000 per task) that are themselves disjoint from the evaluation seeds released in VBVR-MultiStep-Bench.",
+  "rai:dataCollectionType": ["Synthetic"],
+  "rai:dataPreprocessingProtocol": "Per-task generator output is grouped into 50-sample batches and packed into tar.gz shards under questions/. Per-task Parquet metadata files (data/metadata_shards/) and a global metadata.parquet index every instance with task id, family, seed, and per-task fields. No sample is filtered, dropped, or transformed after generation.",
+  "rai:dataAnnotationProtocol": "No human annotation. ground_truth.mp4 is rendered by each task's deterministic ground-truth solver.",
+  "rai:dataAnnotationPlatform": "N/A (no annotation).",
+  "rai:dataReleaseMaintenancePlan": "Versioned releases on the Hugging Face Hub. The Croissant file in this repository is the canonical long-term record. A 5 GB representative subset is provided under sample/ for quick inspection and reviewer convenience.",
+  "rai:dataLimitations": [
+    "Synthetic and stylized: transfer to unconstrained open-world video is not validated.",
+    "Visual rendering is intentionally simplified to keep the symbolic state recoverable from frames; this is not a photorealism corpus.",
+    "Per-task generator parameter ranges are bounded (e.g., maze sizes, planning horizons, physics regimes); the corpus does not span the long tail of any single family.",
+    "Reference rollouts encode one valid trajectory per instance; alternative valid trajectories are not enumerated.",
+    "Although 36 tasks ship, only 34 are used in the training experiments described in the companion paper; this release contains all 36 task families."
+  ],
+  "rai:dataBiases": [
+    "Family balance is uniform (6 tasks per family) by design and does not reflect natural prevalence of these reasoning patterns.",
+    "Generator parameters bias the difficulty distribution toward bounded and seed-controlled regimes that are amenable to symbolic ground truth; rare or open-ended cases are out of scope.",
+    "Visual style is monocular, planar, and rendered by a fixed family of renderers; appearance distribution does not approximate any real-world video corpus and does not contain demographic content.",
+    "No human demographic information is generated; bias along human demographic axes does not apply."
+  ],
+  "rai:personalSensitiveInformation": "None. The dataset contains no personal information, no biometric data, no demographic information, and no human subjects. All visual content is procedurally generated geometric, symbolic, or physical scenes.",
+  "rai:dataUseCases": "Training image-to-video systems on long-horizon multi-step reasoning under explicit per-step rules. Validated use case in the companion paper: fine-tuning Wan2.2-I2V-A14B (Apache-2.0) with Dual-DiT two-phase LoRA. Out-of-scope: production VLM pretraining at scale, real-world video generation, or any safety-critical use.",
+  "rai:dataSocialImpact": "Intended for academic research on reasoning evaluation in video generation. Risks are minimal: the dataset is synthetic, free of personal content, and rendered in a stylized regime not representative of any real population. The most plausible concern is research-direction effects (e.g., over-investing in stylized synthetic benchmarks), which we mitigate by positioning this corpus as a complement to (not a replacement for) appearance-centric and real-world video corpora.",
+  "rai:dataReleaseUpdate": "If post-release errors are discovered, fixes will be published as additive shards or replacement Parquet entries in a new dataset version, with the prior version retained at its commit hash for backwards reproducibility."
+}

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