Reinforcement Learning from Human Feedback (RLHF) has become a crucial
technology for aligning language models with human values and intentions,
enabling models to produce more helpful and harmless responses. Reward models
are trained as proxies for human preferences to drive reinforcement learning
optimization. While reward models are often considered central to achieving
high performance, they face the following challenges in practical applications:
(1) Incorrect and ambiguous preference pairs in the dataset may hinder the
reward model from accurately capturing human intent. (2) Reward models trained
on data from a specific distribution often struggle to generalize to examples
outside that distribution and are not suitable for iterative RLHF training.
In this report, we attempt to address these two issues. (1) From a data
perspective, we propose a method to measure the strength of preferences within
the data, based on a voting mechanism of multiple reward models. Experimental
results confirm that data with varying preference strengths have different
impacts on reward model performance. We introduce a series of novel methods to
mitigate the influence of incorrect and ambiguous preferences in the dataset
and fully leverage high-quality preference data. (2) From an algorithmic
standpoint, we introduce contrastive learning to enhance the ability of reward
models to distinguish between chosen and rejected responses, thereby improving
model generalization. Furthermore, we employ meta-learning to enable the reward
model to maintain the ability to differentiate subtle differences in
out-of-distribution samples, and this approach can be utilized for iterative
RLHF optimization.

从数据和算法的角度出发，本文针对强化学习通过人类反馈进行优化的技术中面临的困难提出了解决方法，包括使用多个奖励模型进行数据评估和投票机制来消除数据中错误和模糊偏好的影响，并引入对比学习和元学习来增强奖励模型的区分能力和泛化能力，从而实现迭代优化。