What is the role of the reward model in RLHF?

What is the role of the reward model in RLHF?

• A. To provide a score that guides optimization of the language model.
• B. To store training data for future use.
• C. To generate the final outputs directly.
• D. To supervise initial data collection only.

Answer: (A)

In RLHF, the reward model acts as a learned judge that scores how good a given model output is according to human preferences. It’s trained on human feedback (for example, comparisons or ratings of multiple outputs) so it learns to approximate what humans would choose. When the base language model generates responses, the reward model assigns a numeric reward to each one. The reinforcement learning loop then optimizes the language model to maximize expected reward, guiding it to produce outputs that align with human judgments. It doesn’t store training data, nor does it generate the final outputs itself—the language model does that. It also isn’t limited to initial data collection; the reward signal is used throughout training to shape the model’s behavior.

In RLHF, the reward model acts as a learned judge that scores how good a given model output is according to human preferences. It’s trained on human feedback (for example, comparisons or ratings of multiple outputs) so it learns to approximate what humans would choose. When the base language model generates responses, the reward model assigns a numeric reward to each one. The reinforcement learning loop then optimizes the language model to maximize expected reward, guiding it to produce outputs that align with human judgments. It doesn’t store training data, nor does it generate the final outputs itself—the language model does that. It also isn’t limited to initial data collection; the reward signal is used throughout training to shape the model’s behavior.