DatasetMan/PM4Bench-QGO-Train

PM4Bench QGO training data

Synthetic multilingual OCR data for QGO reinforcement learning

Overview

The paper Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning uses PM4Bench to show that OCR is a key source of cross-lingual performance gaps when text is rendered visually. QGO addresses that finding with GRPO on synthetic OCR data, without task-specific VQA or GUI supervision. This repository contains the exact training and validation corpus used for the released QGO-8B checkpoint.

Dataset contents

The synthetic multilingual OCR corpus contains:

• 19,500 training rows
• 500 validation rows
• embedded image bytes
• prompt, rule-based reward target, answer, split, and auxiliary metadata

The release builder preserves row order and learning content. It changes only historical machine-specific image path strings to portable basenames. Per-shard source and release SHA-256 values are recorded in metadata/release_inventory.json.

The dataset is synthetic and released under Apache-2.0.

metadata/release_inventory.json records every shard's row count, byte size, source digest, release digest, and the only permitted transformation. The repository-level SHA256SUMS covers the complete public snapshot.

The QGO training recipe includes the launcher and exact reward implementation.

Cite Us

@misc{gao2026benchmarkingboostingmultilingualcapabilities,
      title={Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning},
      author={Junyuan Gao and Jiahe Song and Jiang Wu and Runchuan Zhu and Guanlin Shen and Shasha Wang and Xingjian Wei and Haote Yang and Weijia Li and Bin Wang and Lijun Wu and Conghui He},
      year={2026},
      eprint={2503.18484},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2503.18484v3},
}