"Reinforcement learning is particularly tractable when repeated attempts are inexpensive, the environment can be reset, and success can be evaluated automatically."
Karpathy identifies verifiability as a major factor determining which tasks can be effectively optimized with modern AI training methods. His framework emphasizes resettable environments, efficient repeated attempts, and automated rewards, directly supporting the chapter's distinction between controlled training environments and messy real-world applications.
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