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This paper argues that Explorative Modeling (XM) optimizes for 'freedom' rather than generative expressivity, proving that larger candidate pools increase miss probability and freedom, with empirical results showing freedom-based selection improves generalization under distribution shift.
This paper introduces Explorative Modeling, a new generative modeling paradigm that factors the training loop by exploring candidate matches between model generations and data. It establishes a third pretraining axis beyond parameters and data, improves scaling efficiency across images, video, and language, and enables end-to-end generative modeling with far fewer inference steps.