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The rapid expansion of large-scale pre-trained models has produced systems of remarkable capability, but raw power does not guarantee usefulness. A model trained only to predict the next token or minimize reconstruction error often produces outputs that are technically correct but misaligned with human expectations, intent, or values. Reinforcement Learning with Human Feedback (RLHF) emerged as a method for addressing this gap. It provides a systematic way to take broad general-purpose models and align their behavior with what humans actually prefer.

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Reinforcement Learning with Human Feedback (RLHF)

  • 章节

  • 发表日期：

    2026年

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  • Irena Cronin

摘要

The rapid expansion of large-scale pre-trained models has produced systems of remarkable capability, but raw power does not guarantee usefulness. A model trained only to predict the next token or minimize reconstruction error often produces outputs that are technically correct but misaligned with human expectations, intent, or values. Reinforcement Learning with Human Feedback (RLHF) emerged as a method for addressing this gap. It provides a systematic way to take broad general-purpose models and align their behavior with what humans actually prefer.