Vero-2.5M-unfiltered

# Vero-2.5M-unfiltered > [!Note] > This repository contains the full unfiltered dataset used to construct Vero-600k and Vero-1.6M, before question and answer filtering. > Note that task categories are not balanced in this dataset. <p align="center"> <img src="./vero-logo-blue-transparent.png" alt="Vero" width="520"> </p> Vero is a fully open reinforcement learning (RL) recipe for training and evaluating multi-task visual reasoning with vision-language models. This repository contains the **Vero-2.5M-unfiltered** dataset, a curation of 2.5M reinforcement learning samples from 59 datasets across 6 diverse visual reasoning categories. [](https://github.com/zlab-princeton/vero) [](https://huggingface.co/collections/zlab-princeton/vero) [](https://vero-reasoning.github.io) [](https://huggingface.co/papers/2604.04917) ## Highlights - **Scale**: 2.5M RL samples from 59 datasets. - **Diversity**: Covers 6 broad categories: STEM Reasoning, Chart & OCR, Spatial & Action, Knowledge & Recognition, Grounding & Counting, and Instruction Following. - **Task-Routed Rewards**: Designed to handle heterogeneous answer formats across diverse tasks. - **Open Recipe**: Fully open release of models, training code, evaluation suite, and dataset. ## Dataset Structure The dataset is organized into six broad task categories: 1. **STEM reasoning** 2. **Chart and OCR** 3. **Spatial reasoning and action** 4. **Knowledge and recognition** 5. **Grounding, counting, and visual search** 6. **Captioning and instruction following** For detailed dataset format, curation details, and reward routing metadata, see the [Data Guide](https://github.com/zlab-princeton/vero/blob/main/docs/DATA.md). ## Sample Usage To download and format the Vero-2.5M dataset for RL training using the official setup script (note that you will need to specify this Vero-2.5M-unfiltered in the script): ```bash # Clone the repository git clone https://github.com/zlab-princeton/vero.git cd vero # Run the formatting script python scripts/download_and_format_vero_600k.py ```