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Real-world robot policies can learn autonomously from sparse rewards by treating value predictions as task-progress memory, outperforming offline training by $32.8\%$ success rate.
Robots can now evolve their own code, learning from task execution to autonomously repair and reuse skills, achieving superior performance on complex manipulation benchmarks.