| --- |
| 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 |
|
|
| <div align="center"> |
|
|
| [](https://arxiv.org/abs/2605.20342) |
| [](https://evolvinglmms-lab.github.io/ParaVT/) |
| [](https://github.com/EvolvingLMMs-Lab/ParaVT) |
| [](https://huggingface.co/datasets/ParaVT/ParaVT-Parquet) |
| [](https://huggingface.co/ParaVT/ParaVT-8B) |
| [](https://huggingface.co/papers/2605.20342) |
|
|
| </div> |
|
|
| Source media archives for the [ParaVT](https://github.com/EvolvingLMMs-Lab/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. 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`](https://github.com/EvolvingLMMs-Lab/ParaVT/blob/main/paravt/data/materialize.py) 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`](https://huggingface.co/datasets/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 |
|
|
| ```bash |
| # 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 |
|
|
| ```bibtex |
| @misc{yang2026paravt, |
| title={{ParaVT}: Taming the Tool Prior Paradox for Parallel Tool Use in Agentic Video Reinforcement Learning}, |
| author={Zuhao Yang and Kaichen Zhang and Sudong Wang and Keming Wu and Zhongyu Yang and Bo Li and Xiaojuan Qi and Shijian Lu and Xingxuan Li and Lidong Bing}, |
| year={2026}, |
| eprint={2605.20342}, |
| 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. |
|
|
