Ruijin Jin

Rosetta Stone For Unified MLLMs: A Unified Tokenizer to Decipher Understanding and Generation

Major state-of-the-art unified tokenizers predominantly adopt pixel reconstruction and feature alignment as pretext tasks, they leave key domains largely unexplored such as architecture, supervised objectives and tasks interaction, potentially resulting in limited performance. We systematically inve