Reward Model Training — Learn Preferences From Comparison Data

I will train a reward model from your human preference comparison data — fine-tuning a language model to predict which of two model outputs a human annotator would prefer, producing a scalar reward signal for the RL training phase. Reward model quality is the single most critical determinant of RLHF success — a reward model that doesn't reliably capture human preferences will train the LLM toward proxy behaviours that score well on the reward model while failing to satisfy the actual human preference objective, the phenomenon known as reward hacking.

The training covers preference pair dataset formatting, reward model architecture configuration (Bradley-Terry or ranking head on a language model backbone), training with pairwise preference loss, validation accuracy evaluation on held-out preferences, reward score calibration, and delivery of the trained reward model.