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---
dataset_info:
features:
- name: images
sequence: image
- name: problem
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 167434471.0
num_examples: 2000
download_size: 166955903
dataset_size: 167434471.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This is the official release of the training data for paper **PAPO: Perception-Aware Policy Optimization for Multimodal Reasoning**. (arxiv.org/abs/2507.06448)
(Optional) This dataset can be used as the `val` split of the training dataset for PAPO. You may find the full training dataset at [PAPOGalaxy/PAPO_ViRL39K_train](https://huggingface.co/datasets/PAPOGalaxy/PAPO_ViRL39K_train).
# Data Source
## **Training**
- We adapt the multimodal benchmark [TIGER-Lab/ViRL39K](https://huggingface.co/datasets/TIGER-Lab/ViRL39K) to construct our PAPO training dataset.
## **Validation (Optional)**
- (Optional) We use the `test` set from [FanqingM/MMK12](https://huggingface.co/datasets/FanqingM/MMK12) for validation during training.
- Note that this is solely for monitoring. We do not pick checkpoints based on this in our paper.
# Dataset Structure
- **train:** training set consisting of **38870** multimodal reasoning samples
- **val:** validation set consisting of **2000** multimodal reasoning samples
# Data Fields
- **id:** data id
- data type: String
- **problem:** input question or statement
- - data type: String
- **images:** input image(s)
- data type: List
- **answer:** ground-truth answer
- - data type: String
# Usage
To use the full dataset with both `train` and `val` split, you may code as follows:
```python
# Train
train_dataset = load_dataset("PAPOGalaxy/PAPO_ViRL39K_train")
# Val
val_dataset = load_dataset("PAPOGalaxy/PAPO_MMK12_test")
``` |