README.md

---
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")
```