- Explorative Modeling (XM) introduces exploration (K guesses) to solve the many-answers problem by modifying the training objective so that only the best match trains.
- XM acts as a third pretraining axis alongside parameters and data, monotonically improving existing generative models across images, video, and language with gains growing at scale.
- XM achieves 6.2× sample efficiency, 4.1× FLOP efficiency, and 47% better parameter efficiency, and enables end-to-end generation matching diffusion with up to 256× less inference compute.
- The concept of generative expressivity (number of distinct answers a model can capture) is introduced and shown to be scalable via exploration, directly impacting performance.