---
license: apache-2.0
task_categories:
  - video-text-to-text
  - visual-question-answering
language:
  - en
tags:
  - video
  - long-video
  - reasoning
  - tool-calling
  - multimodal
size_categories:
  - 100K<n<1M
---

# ParaVT-Source

Source media archives for the [ParaVT](https://github.com/mwxely/ParaVT) training corpus. Pair this repository with the annotations in [`ParaVT/ParaVT-Parquet`](https://huggingface.co/datasets/ParaVT/ParaVT-Parquet).

## Overview

ParaVT is a multi-agent agentic framework for long-video understanding, post-trained with **PARA-GRPO** (Parseability-Anchored and Ratio-gAted GRPO). This dataset bundles the raw video files referenced by every row in `ParaVT-Parquet`, packaged as per-source zip archives.

## Layout

Files are grouped by sentinel bucket (matching the path scheme used by [`paravt.data.sanitize`](https://github.com/mwxely/ParaVT/blob/paravt-release/paravt/data/sanitize.py)). Each bucket's archives extract directly into a directory tree that the [`paravt.data.materialize`](https://github.com/mwxely/ParaVT/blob/paravt-release/paravt/data/materialize.py) helper can re-attach to absolute paths in a single call.

| Bucket | Contents | Archive convention |
|---|---|---|
| `longvt_source/<src>/` | LongVT shared training clips (`videor1_*`, `longvideoreason_*`, `geminicot_*`, `tvg_*`, `selftrace_*`) | `<src>_<idx>.zip`, mirroring the [`longvideotool/LongVT-Source`](https://huggingface.co/datasets/longvideotool/LongVT-Source) naming |
| `museg/charades/` | Charades-STA clips used by the `charades` SFT split and the `charades_tvg` RL split | `charades_<idx>.zip` |
| `museg/et_instruct_164k/` | MuSeG `et_instruct_164k` clips used by the `museg` SFT split | `et_instruct_<idx>.zip` |
| `selfqa/` | Self-curated open-ended QA clips (HACS- and Ego4D-derived UUIDs / YouTube IDs) used by the `hacs` and `ego4d_naq` RL splits | `selfqa.zip` (single archive, ~6 GB) |

Each archive is sized to stay below 10 GB on disk so that LFS pointer + Cloudflare CDN serving stays well-behaved.

## Usage

```bash
huggingface-cli download ParaVT/ParaVT-Source --repo-type dataset --local-dir ./paravt-source

# Selective: pull only the buckets you need (e.g. charades grounding only)
huggingface-cli download ParaVT/ParaVT-Source \
    --repo-type dataset --local-dir ./paravt-source \
    --include "museg/charades/*"

# Extract zips in place
( cd ./paravt-source && for z in */*.zip; do unzip -q -d "$(dirname "$z")" "$z"; done )

# Re-link absolute paths inside the parquets (one shot; see ParaVT/ParaVT-Parquet)
python -m paravt.data.materialize \
    --root        ./paravt-source \
    --parquet-dir ./paravt-parquet \
    --output-dir  ./paravt-parquet-materialized
```

## Citation

```bibtex
@misc{yang2026paravt,
  title={{ParaVT}: From Format Fragility to Parallel Tool Mastery in Agentic Video {RL}},
  author={Zuhao Yang and others},
  year={2026},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}
```

## Acknowledgements

The `longvt_source/` archives reuse subsets of the [LongVT](https://github.com/EvolvingLMMs-Lab/LongVT) training media released at [`longvideotool/LongVT-Source`](https://huggingface.co/datasets/longvideotool/LongVT-Source); the MuSeG, Charades-STA, HACS, and Ego4D source clips are attributed to their respective original publications and used under their original licenses.