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QWM achieves unprecedented sample efficiency in reinforcement learning by leveraging world models without succumbing to compounding bias, outperforming prior methods on challenging benchmarks.
Simulation-based pre-training can drastically improve the dexterity of robotic hands, outperforming traditional training methods with just a fraction of real-world data.
