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How does reinforcement learning shape a language model’s internal representations? We present evidence that RL recruits a pre-existing representation of functional welfare: an estimate of how well or badly the system is doing, relative to its goals. We train several language models in a novel, semantically neutral maze environment. We then extract concept vectors for rewarded and punished trajectories, and evaluate those vectors in settings unrelated to the maze environment. The punishment vecto

Technistema - Brad Lindblad, MBA

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大阪大学大学院理学研究科 凝縮系量子計算グループ

The Bash-Operated Reconciling Kludge

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RainChan的小博客