Generalist GEN-1.5 少样本学习能力
For few-shot learning, it can adapt to new physical tasks in as few as 1 - 10 gradient steps on 1 - 5 minutes of data (~10 - 50 demonstrations). In practice, th
AI 导读
在少样本学习方面，它只需 1 - 10 个梯度步、1 - 5 分钟数据（约 10 - 50 次演示）即可适应新的物理任务。实践中，这可以描述为低数据条件下的测试时训练。我们没有调优这一流程，也没有做超参数扫描；这些结果基本是开箱即用的。
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For few-shot learning, it can adapt to new physical tasks in as few as 1 - 10 gradient steps on 1 - 5 minutes of data (~10 - 50 demonstrations). In practice, this can be described as test-time training in a low-data regime. We did not tune this procedure or sweep hyperparameters; these results come largely out of the box.