DatasetMan/QGO-8B
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QGO-8B
OCR-centric reinforcement learning for multilingual LVLMs
    
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Overview
The paper *Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning* introduces the strictly parallel PM4Bench benchmark and finds that OCR is a key source of cross-lingual performance gaps under vision-only input. QGO trains on fully synthesized OCR data with a dual reward for recognition accuracy and reasoning-preserving response structure. It improves average score on every PM4Bench task and setting and transfers gains to external multilingual OCR benchmarks.
QGO-8B is the OCR-centric GRPO checkpoint derived from `Qwen/Qwen3-VL-8B-Thinking`. This repository contains the complete global-step-200 BF16 checkpoint used in the PM4Bench paper.
Model details
- Architecture:
Qwen3VLForConditionalGeneration - Base model:
Qwen/Qwen3-VL-8B-Thinking - Training method: GRPO
- Released checkpoint: global step 200
- Weight format: safetensors, four BF16 shards
- Indexed tensor bytes: 17,534,247,392
- License: Apache-2.0
- Tested Transformers version: 4.57.6
Usage
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
model_id = "DatasetMan/QGO-8B"
model = Qwen3VLForConditionalGeneration.from_pretrained(
model_id,
dtype="auto",
device_map="auto",
)
processor = AutoProcessor.from_pretrained(model_id)The full BF16 weights are approximately 17.5 GB before runtime allocations. Plan GPU/CPU memory for weights, vision inputs, KV cache, and generation in addition to the checkpoint size.
Use the base model's official chat template and greedy decoding for PM4Bench evaluation. Task prompts and evaluation code are provided in <https://github.com/opendatalab/PM4Bench>.
Training
The released data is available at <https://huggingface.co/datasets/DatasetMan/PM4Bench-QGO-Train>. The recipe uses 32 prompts and 8 rollouts per prompt (256 trajectories per step), AdamW with learning rate 1e-6, BF16, and eight GPUs.
PM4Bench evaluation
These are the audited paper results. MDUR and MGUI are percentages, MIQA is the six-dimension judge score on a 10-100 scale, and MSOCR is on a 0-40 scale.
Limitations
QGO-8B targets multilingual OCR robustness. It inherits limitations and risks from the Qwen base model and is not guaranteed to improve every downstream task or language. Coordinate outputs, OCR transcriptions, and long-form reasoning should be validated before use in consequential applications.
Cite Us
<pre><code class="language-bibtex"> @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}, } </code></pre>