Emergence of Text Readability in Vision Language Models

We investigate how the ability to recognize textual content within images emerges during the training of Vision-Language Models (VLMs). Our analysis reveals a critical phenomenon: the ability to read textual information in a given image $\mathbf{\text{(text}}\mathbf{\text{readability)}}$ emerges abruptly after substantial training iterations, in contrast to semantic content understanding which develops gradually from the early stages of training. This delayed emergence may reflect how contrastive learning tends to initially prioritize general semantic understanding, with text-specific symbolic processing developing later. Interestingly, the ability to match images with rendered text develops even slower, indicating a deeper need for semantic integration. These findings highlight the need for tailored training strategies to accelerate robust text comprehension in VLMs, laying the groundwork for future research on optimizing multimodal learning.