In machine learning, reinforcement learning from human feedback (RLHF) is a method of training AI models by learning from responses by humans about its performance. If an AI model makes a prediction or takes an action that is incorrect or suboptimal, human feedback can be used to correct the error or suggest a better response.
Over time, this helps the model to learn and improve its responses. RLHF is used in tasks where it’s difficult to define a clear, algorithmic solution but where humans can easily judge the quality of the AI’s output (e.g. if the task is to generate a compelling story, humans can rate different AI-generated stories on their quality, and the AI can use their feedback to improve its story generation skills).
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The Daily Omnivore 