Benchmarking and Boosting Multilingual Capabilities of LVLMs via OCR-Centric Reinforcement Learning

Evaluating the multilingual capabilities of Large Vision-Language Models (LVLMs) remains challenging because most benchmarks rely on non-parallel corpora, making it unclear whether cross-lingual performance gaps reflect model limitations or dataset inconsistencies. To address this, we introduce PM4Bench, the first multimodal, multilingual, multi-task benchmark built on a strictly parallel 10-language corpus, enabling fair, apples-to-apples cross-lingual comparison of model performance. We further introduce a vision setting that embeds textual inputs directly into images, better approximating deployment scenarios where LVLM-driven agents interact with virtual or physical environments through unified visual observations. Experiments with 10 LVLMs reveal that OCR is a key factor behind cross-lingual disparity when textual content is rendered visually. Motivated by this, we design an OCR-centric GRPO training strategy using fully synthesized, label-free OCR data, without expensive task-specific VQA supervision. The resulting model improves general multilingual VQA capability, reduces cross-lingual disparities under the vision setting, and transfers gains beyond PM4Bench. This methodology offers an efficient, label-free pathway toward more equitable multilingual deployment of LVLM-driven agents.