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

我们提出一种改进奖励模型质量的新方法，通过生成合成偏好数据，以使训练数据集增加基于策略且高质量的偏好对。经验证明，该方法可以改善任何奖励模型的性能，效果与添加相似数量的人类偏好数据相当。这项工作为改进语言模型对齐的强化学习的成功开辟了新的研究领域，提供了合成偏好生成作为解决奖励模型建模挑战的方案。

改进奖励建模的西至N:合成优先性生成