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As a reinforcement learning methodology, the central objective of Reinforcement Learning from Human Feedback (RLHF) is to develop a policy—the language model—that learns to generate outputs in a way that maximizes a reward signal. This reward is derived from the environment, which in this context is structured to reflect human preferences.

Goal of RLHF

An AI development team is refining a language model. Their process involves showing human reviewers two different model-generated responses to the same prompt and asking them to select the better one. This preference data is then used to further train the model. What is the fundamental goal of this training methodology?

The central objective of Reinforcement Learning from Human Feedback (RLHF) is to train a language model to produce the most factually correct and verifiable information.

In the context of training a language model using a methodology based on reinforcement learning and human feedback, describe the central objective the model is trained to achieve. What does it seek to maximize, and where does the signal for this maximization originate?

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