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ngqtrung
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Model Details
Model Provider
ngqtrung
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Accuracy in percent, evaluated with lmms-eval at 100 frames per video under one setting for every
row: at most 501,760 pixels per frame, a 33,792-token context, and at most 16,384 generated tokens.
"In-domain" is the 1,000-question held-out Video-HopChain split.
| Model | Video-MME | PerceptionComp | Video-MMMU | Video-Holmes | VCRBench | MMR-V | LongVideo-Reason | VRBench | mean | in-domain |
|---|---|---|---|---|---|---|---|---|---|---|
| Qwen3-VL-8B-Instruct | 64.0 | 28.1 | 63.5 | 40.7 | 32.0 | 42.7 | 72.6 | 74.7 | 52.3 | 13.4 |
| this model (+ standard RL) | 65.8 | 34.3 | 63.0 | 47.4 | 35.9 | 43.4 | 76.1 | 77.6 | 55.4 | 13.4 |
| Video-HopChain-8B (final) | 69.2 | 37.4 | 67.5 | 48.6 | 44.9 | 46.6 | 78.9 | 81.0 | 59.3 | 23.2 |
VCRBench is reported on its multiple-choice subset.
python
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration model = Qwen3VLForConditionalGeneration.from_pretrained( "ngqtrung/video-hopchain-8b-standard-rl", dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("ngqtrung/video-hopchain-8b-standard-rl")The weights are BF16. The model was trained to reason inside <think>...</think> and to put the final
answer in <answer>\boxed{...}</answer>, so use the same system prompt as training. It is in the
dataset rows.
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}}from transformers import AutoProcessor, Qwen3VLForConditionalGeneration model = Qwen3VLForConditionalGeneration.from_pretrained( "ngqtrung/video-hopchain-8b-standard-rl", dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained("ngqtrung/video-hopchain-8b-standard-rl")@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}}