Reinforcement learning leaves models sharply jagged: on the rails of a verifiable domain they are superintelligent, off them everything meanders.

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• A verifiable task can be optimised by reinforcement learning until a neural network performs it extremely well. AKAndrej Karpathy17 Nov 2025reinforcement learning
• Capability does not generalise for free: a model that will move mountains on an agentic task still tells the same bad joke it told five years ago. AKAndrej Karpathy20 Mar 2026reinforcement learning
• Current models are unsafe because they are not smart enough, not because they are too smart. FCFrançois Chollet13 Sept 2026reinforcement learning
• Humans barely use reinforcement learning for intelligence — what RL they do use goes into motor tasks, not problem solving. AKAndrej Karpathy17 Oct 2025reinforcement learning
• Humans keep their place in AI as judges rather than authors, because telling which of two answers is better is far easier than writing a good one. NLNathan Lambert3 Feb 2025reinforcement learning

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