Datasets:

ParaVT
/

ParaVT-Source

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
viewer: false

ParaVT-Source

Source media archives for the ParaVT training corpus. Pair this repository with the annotations in 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). Each archive's members are stored under their full sentinel-form path, so extracting every zip into a single root produces a unified tree that paravt.data.materialize can re-link in one 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` naming
`longvt_source/videor1_<N>/...` (images)	Auxiliary image files (`.png` / `.jpg`) referenced by the multi-modal interleaved rows in `paravt_sft_videor1_50k.parquet`	`videor1_<N>_images.zip` (one per `videor1_<N>` bucket)
`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, ~3 GB)

Each archive is sized to stay below 10 GB on disk so that LFS pointer + Cloudflare CDN serving stays well-behaved. The <src>_<idx>.zip and videor1_<N>_images.zip archives that share a videor1_<N> prefix unzip into disjoint subdirectories of the same videor1_<N>/ tree (videos under CLEVRER/, LLaVA-Video-178K/, … and images under Chart/, Math/, Knowledge/, OCR/, …), so they don't overwrite each other.

Usage

# 1. Download every archive (use --include for a subset; see below).
huggingface-cli download ParaVT/ParaVT-Source --repo-type dataset --local-dir ./paravt-source

# 2. Extract every zip into the same root. Each zip's arcname carries the
#    full sentinel path (e.g. "longvt_source/videor1_7/Math/...png"), so the
#    extraction target must be the top-level root, NOT the per-zip directory.
( cd ./paravt-source && find . -name "*.zip" -exec unzip -q -o -d . {} \; )

# 3. 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

# Selective: pull only the buckets you need (e.g. Charades grounding only).
# Materialize will warn on missing files but produce valid output for the
# subset that is present.
huggingface-cli download ParaVT/ParaVT-Source \
    --repo-type dataset --local-dir ./paravt-source \
    --include "museg/charades.zip"

After step 2 the directory tree under ./paravt-source/ looks like:

paravt-source/
├── longvt_source/
│   ├── videor1_<N>/             # mp4 from videor1_<N>.zip + png/jpg from videor1_<N>_images.zip
│   │   ├── CLEVRER/, LLaVA-Video-178K/, NeXT-QA/, ...   # videos
│   │   └── Chart/, Math/, Knowledge/, OCR/, ...         # images
│   ├── longvideoreason_<N>/     # from longvideoreason_<N>_part{1,2}.zip
│   ├── geminicot_<N>/, tvg_<N>/, selftrace_<N>/, ...
├── museg/
│   ├── charades/                # mp4 from museg/charades.zip
│   └── et_instruct_164k/        # mp4 from museg/et_instruct.zip
└── selfqa/                      # mp4 from selfqa/selfqa.zip

The materialized parquets then point at file://<absolute-path-to-paravt-source>/<sentinel-path> for every row.

Citation

@misc{yang2026paravt,
  title={{ParaVT}: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning},
  author={Zuhao Yang and Sudong Wang and Kaichen Zhang and Keming Wu and Sicong Leng and Yifan Zhang and Bo Li and Chengwei Qin and Shijian Lu and Xingxuan Li and Lidong Bing},
  year={2026},
  archivePrefix={arXiv},
  primaryClass={cs.CV}
}

Acknowledgements

The longvt_source/ archives reuse subsets of the LongVT training media released at 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.