From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation
Building image generators by teaching skills in the right order
Researchers built a new system for training image-generation AI that organizes training data around learning dependencies—teaching fundamental skills before advanced ones—rather than treating each task separately. The approach created a 440-million-image dataset and produced models that handle text-to-image generation and image editing with broader visual coverage and better skill transfer than conventional methods.
Image generators trained this way perform better across multiple tasks without needing separate specialized models, reducing computational waste and engineering overhead. This suggests that how data is sequenced during training matters as much as the data itself—a finding that could improve efficiency in training other large AI systems.