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Forget static datasets - co-evolving LLMs and tasks unlocks a broader range of expert capabilities than hand-crafted training pipelines, all while using less GPU memory.
Scaling test-time compute can dramatically improve the success rate of robot imitation learning, achieving up to 95% on complex manipulation tasks.
