Large Multimodal Models (LMMs) often face a modality representation gap during pretraining: while language embeddings remain stable, visual representations are highly sensitive to contextual noise (e.g., background clutter). To address this issue, we introduce a visual comprehension stage, which we call \textbf{ViCToR}~(\textbf{Vi}sual \textbf{C}omprehension via \textbf{To}ken \textbf{R}econstruction), a novel pretraining framework for LMMs. ViCToR employs a learnable visual token pool and utilizes the Hungarian matching algorithm to select semantically relevant tokens from this pool for visual token replacement. Furthermore, by integrating a visual token reconstruction loss with dense semantic supervision, ViCToR can learn tokens which retain high visual detail, thereby enhancing the large language model’s (LLM’s) understanding of visual information. After pretraining on 3 million publicly accessible images and captions, \textbf{ViCToR} achieves state-of-the-art results, improving over LLaVA-NeXT-8B by $10.4\%$, $3.2\%$, and $7.2\%$ on the MMStar, SEED$^{I}$, and RealWorldQA benchmarks, respectively. We will release the code and model weights to facilitate reproducibility.