metadata
dataset_info:
features:
- name: images
sequence: image
- name: problem
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 55927614.59
num_examples: 8030
- name: test
num_bytes: 88598361.32
num_examples: 3040
download_size: 107933739
dataset_size: 144525975.91
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
license: mit datasets: - Yuting6/geoqa-r1v-augmentation - Yuting6/math-8k-augmentation - Yuting6/m3cot-augmentation - Yuting6/TQA-augmentation - Yuting6/Geo3k-augmentation - Yuting6/geoqa-r1v-noise - Yuting6/geoqa-r1v-crop - Yuting6/geoqa-r1v-blur - Yuting6/geoqa-r1v-8k-rotated - Yuting6/geoqa-r1v-8k-mixup base_model: - Qwen/Qwen2.5-VL-7B-Instruct
Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning
Paper Title and Link
The model was presented in the paper Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning. You can also find the paper on arXiv: Vision Matters: Simple Visual Perturbations Can Boost Multimodal Math Reasoning (arXiv:2506.09736)
Paper Abstract
Vision-Matters is a simple visual perturbation framework that can be easily integrated into existing post-training pipelines including SFT, DPO, and GRPO. Our findings highlight the critical role of visual perturbation: better reasoning begins with better seeing.
- 🐙 GitHub Repo: YutingLi0606/Vision-Matters
- 💾 Dataset: Yuting6/vision-matters on Hugging Face