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)
• Model: ngqtrung/video-hopchain-8b
• Project page: ngquangtrung57.github.io/video-hopchain-page
• Code: github.com/ngquangtrung57/video-hopchain
• Collection: 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 50,000 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

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:

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. A difficulty filter finally drops questions that the base model solves in more than half 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

@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}
}