Question

In Reinforcement Learning from Human Feedback (RLHF), what is the function of the reward model that is trained before the final policy optimization step?

Accepted Answer

In Reinforcement Learning from Human Feedback, the reward model acts as a proxy for human judgment to automate the evaluation of model outputs. During the training of a language model, it is difficult to mathematically define what makes a response good because human preferences are nuanced. To solve this, researchers collect a dataset of responses ranked by humans from best to worst. The reward model is a smaller neural network trained on this data to predict a numerical score for any given model output that reflects how much a human would prefer it. Once trained, the reward model functions as a scoring mechanism during the final policy optimization step, which typically uses an algorithm like Proximal Policy Optimization. Instead of requiring a human to manually rate every response during this stage, the reinforcement learning algorithm sends generated text to the reward model, receives the predicted score, and uses that score as a reward signal to update the language model. This process shifts the model toward generating text that yields higher scores, effectively aligning the model&#x27;s behavior with the preferences captured by the reward model without needing constant human intervention.

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In Reinforcement Learning from Human Feedback (RLHF), what is the function of the reward model that is trained before the final policy optimization step?