Website: https://explorative-modeling.github.io/ GitHub: https://github.com/alexiglad/XM TLDR : We introduce Explorative Modeling, a new paradigm for generative modeling that acts as a third pretraining axis when added to existing generative models, and also enables end-to-end generation. Increasing exploration monotonically improves existing models across images, video, and language, and the gains grow with scale (7%→36% with data, 13%→23% with parameters). Concretely, Explorative Models (XMs) reach 6.2× sample efficiency, 4.1× FLOP efficiency, and 47% better parameter efficiency. Exploration also enables scaling generalization, and scaling how end-to-end existing models are. As end-to-end generative models, XMs match diffusion on control tasks with up to 256× less inference compute. Let me start with a question that sounds simple. If I ask a model to “generate a dog”, how many correct answers are there? It turns out there are a lot… likely billions or more images that we could count as dog images.…