Multimodal Models and Specialist LLMs

Explain how vision encoders produce visual tokens that can be processed by a language model

Describe unified embedding spaces where text, image, and audio share a common latent representation

Compare GPT-4o's native multimodal approach with pipeline-based systems that chain separate models

Describe how domain-specific pretraining and data filtering create specialist models like Qwen 2.5-Coder and Qwen 2.5-Math

Explain the Code-as-Policy concept where a planner outputs executable code rather than raw trajectory points