How Multimodal Transformers Are Trained to Process Images Alongside Text

Modern large language models can process images by combining three components: a vision encoder that converts pixels into vectors, a connector that translates those vectors into a format the language model understands, and the LLM itself which reasons over both visual and text inputs together. A key engineering insight is that an LLM does not need to interpret raw pixels directly — it only needs to work with a representation produced by a separate vision model. Processing every pixel as a token is computationally impractical, since full self-attention costs scale quadratically with sequence length, making compression essential. The foundation for this approach was the Vision Transformer, introduced by Google Research in 2020, which showed that images could be divided into small patches and fed into a transformer just like word tokens. This architectural pattern suggests that adding new sensory capabilities to existing models often requires a well-designed representation interface rather than retraining the entire model from scratch.