What function does the Reward Model (RM) serve in the Reinforcement Learning from Human Feedback (RLHF) process?
Answer
It predicts which response a human would prefer, acting as a proxy for the human judge.
The Reward Model (RM) is a critical component trained specifically during the RLHF pipeline to bypass the difficulty of hand-engineering complex, subjective reward functions. Human labelers collect comparison data by ranking various model outputs for the same prompt. This human preference data is then used to train the separate Reward Model. Once trained, the RM functions as a proxy for the human judge, generating a scalar score that estimates the relative goodness or preference level of a generated response. This score is then used as the reward signal when fine-tuning the original large language model using reinforcement learning algorithms like PPO.
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