מה בא אחרי RLHF?

תקציר מקורי באנגליתRLHF made models that are extraordinary at pleasing the human in the loop, and Diogo Almeida, a GPT-4 co author, argues that is exactly the problem. Optimizing for human preference optimizes for engagement and for overpromising, the same pressure that makes a model confidently agree that a fart audio file is a symphony. That produces two camps: one where models act as assistants with a human catching mistakes, where RLHF shines, and one where they operate autonomously with real stakes, where the same instinct to please quietly becomes a liability. So what comes next is not the Claude Code era but a shift in what you optimize. Almeida frames it through Sutton's bitter lesson: the task matters more than the data, and reinforcement learning with verifiable rewards points the model at real aut