How to Train a Reward Model for RLHF
A practical guide to reward model training for ML engineers: preference data structure and quality requirements, Bradley-Terry pairwise loss and training objective, LoRA-based reward model fine-tuning, pairwise accuracy as a training metric, reward hacking failure modes and mitigations, best-of-N sampling as a simpler alternative to PPO, and when to use rejection sampling fine-tuning versus full RLHF.