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Latent world models scale predictably with compute via a second-order power law, enabling accurate forecasting of robotic planning performance and zero-shot task execution on real hardware.
Discriminator-Guided RL achieves a remarkable reduction in FID from 9.38 to 2.62, showcasing a new way to align model outputs with real data without human preferences.
LLMs can significantly improve their performance on complex tasks like math and coding *without any external rewards*, simply by iteratively comparing and refining their own outputs.
