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The success of reinforcement learning from human feedback (RLHF) in languagemodel alignment is strongly dependent on the quality of the underlying rewardmodel. In this paper, we present a novel approach to improve reward modelquality by generating synthetic preference data, thereby augmenting thetraining dataset with on-policy, high-quality preference pairs. Motivated bythe promising results of Best-of-N sampling strategies in language modeltraining, we extend their application to reward model training. This results ina self-training strategy to generate preference pairs by selecting the best andworst candidates in a pool of responses to a given query. Empirically, we findthat this approach improves the performance of any reward model, with an effectcomparable to the addition of a similar quantity of human preference data. Thiswork opens up new avenues of research for improving RLHF for language modelalignment, by offering synthetic preference generation as a solution to rewardmodeling challenges.

West-of-N: Synthetic Preferences for Self-Improving Reward Models