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Forget data quantity, diversity is the secret sauce: scaling the variety of tool-use patterns in training data boosts LLM generalization by +22 points on OOD benchmarks, even with 4x less data.
Learning from a mix of real and simulated data can be effectively transferred to real-world robot tasks using progressive neural networks, enabling safer and more efficient online adaptation.
