| --- |
| license: other |
| license_name: source-dataset-terms |
| task_categories: |
| - video-text-to-text |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - arxiv:2609.25773 |
| - video |
| - multi-hop-reasoning |
| - reinforcement-learning |
| - rlvr |
| - grpo |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: videohopchain_train.parquet |
| - split: validation |
| path: videohopchain_val.parquet |
| --- |
| |
| # Video-HopChain |
|
|
| Video-HopChain is a dataset of multi-hop video questions for reinforcement learning with verifiable |
| rewards (RLVR). Every question chains three to six yes/no sub-questions ("hops") about moments in one |
| video. Each hop carries two integers, of which the first counts when the answer to the hop is yes and |
| the second when the answer is no. The model adds the integers of all hops, and that sum is the answer, |
| so an exact match on one number gives a verifiable reward. |
|
|
| The integers of a question are resampled until every combination of hop answers gives a different sum. |
| A wrong intermediate hop therefore changes the total, which is what forces the chain to hold. |
|
|
| - **Paper:** *Video-HopChain: Multi-Hop Questions and Confidence-Gated Exploration for Video Reasoning Models* ([arXiv:2609.25773](https://arxiv.org/abs/2609.25773)) |
| - **Model:** [ngqtrung/Video-HopChain-8B](https://huggingface.co/ngqtrung/Video-HopChain-8B) |
| - **Standard-RL checkpoint:** [ngqtrung/Video-HopChain-8B-Standard-RL](https://huggingface.co/ngqtrung/Video-HopChain-8B-Standard-RL) |
| - **Project page:** [ngquangtrung57.github.io/video-hopchain-page](https://ngquangtrung57.github.io/video-hopchain-page/) |
| - **Code:** [github.com/ngquangtrung57/video-hopchain](https://github.com/ngquangtrung57/video-hopchain) |
| - **Collection:** [Video-HopChain](https://huggingface.co/collections/ngqtrung/video-hopchain) |
|
|
| ## Contents |
|
|
| | File | Rows | Size | |
| |---|---|---| |
| | `videohopchain_train.parquet` | 22,550 | 15.7 MB | |
| | `videohopchain_val.parquet` | 1,000 | 757 KB | |
| | `videohopchain_videos_longvila.zip` | — | 17.2 GiB | |
| | `videohopchain_videos_finevideo.zip` | — | 11.6 GiB | |
| | `videohopchain_videos_vript.zip` | — | 6.1 GiB | |
|
|
| The training split covers 13,378 videos. The held-out split holds 1,000 questions over 1,000 further |
| videos, and it is disjoint from the training split by video id. Every source video lasts at least |
| three minutes. The held-out split is stratified by hop count, by video duration and by source. |
|
|
| The video archives unpack to `videos/<source>/<id>.mp4`, which is the path that the `videos` field of |
| each row already points at. The clips are downscaled proxies, sampled at 140 frames and at most |
| 50,176 pixels per frame, which is the resolution the questions were written and verified against. |
|
|
| ## Row format |
|
|
| Each row follows the verl RLVR layout. |
|
|
| | Field | Meaning | |
| |---|---| |
| | `prompt` | chat messages, the system prompt and the question | |
| | `videos` | one entry with `video` (relative path), `fps`, `max_frames`, `max_pixels`, `min_pixels` | |
| | `data_source` | `hopchain_finevideo`, `hopchain_longvila` or `hopchain_vript` | |
| | `ability` | always `video_multihop_reasoning` | |
| | `reward_model` | `ground_truth` (the integer sum as a string) and `style` (`rule`) | |
| | `extra_info` | `answer`, `num_hops`, `hop_types`, `question_id`, `video_id`, `solve_rate`, `difficulty_band`, `tolerance` | |
|
|
| `tolerance` is `0.0`: the reward is an exact match on the sum. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("ngqtrung/Video-HopChain") |
| print(ds["train"][0]["prompt"][-1]["content"]) |
| print(ds["train"][0]["reward_model"]["ground_truth"]) |
| ``` |
|
|
| Download and unpack the videos beside the parquets: |
|
|
| ```bash |
| hf download ngqtrung/Video-HopChain --repo-type dataset --local-dir video-hopchain |
| cd video-hopchain && for z in videohopchain_videos_*.zip; do unzip -q "$z"; done |
| ``` |
|
|
| ## How the questions were built |
|
|
| Source videos come from FineVideo, from LongVILA as released with the OneThinker training data, and |
| from the long split of Vript. Each video is cut into shots with PySceneDetect and every shot is |
| captioned once with MiniMax-M3. A language model then writes two candidate questions per video from |
| the captions and a sampled specification, and a judge loop checks each candidate against the captions |
| and rewrites it up to three times. The pipeline then ends with a difficulty filter that drops |
| questions the base model already solves in 6 of 8 rollouts. The code for all of this is in the |
| repository linked above. |
|
|
| Because the questions are written from captions rather than from the frames, a caption error can |
| reach a label. We report this limitation in the paper. |
|
|
| ## Licensing |
|
|
| The questions and the annotations in the parquet files are released for research use. |
|
|
| The video archives re-distribute clips from FineVideo, LongVILA and Vript, as downscaled proxies. |
| Those clips remain under the terms of their original datasets and of the original uploaders, and this |
| repository does not grant any right beyond them. Check the source datasets before you use the video |
| for anything other than research, and contact us if you hold rights to a clip and want it removed. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{nguyenquang2026videohopchain, |
| title = {Video-HopChain: Multi-Hop Questions and Confidence-Gated Exploration for Video Reasoning Models}, |
| author = {Nguyen Quang, Trung and Dong, Yuhao and Sun, Shuo and Liu, Shuai and Tian, Shulin and Yap, Kim-Hui and Liu, Ziwei}, |
| year = {2026}, |
| eprint = {2609.25773}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CV}, |
| url = {https://arxiv.org/abs/2609.25773} |
| } |
| ``` |
|
|
