The paper addresses challenges in Reinforcement Learning from Human Feedback (RLHF) by proposing methods to mitigate incorrect and ambiguous preferences in the dataset and improve model generalization using contrastive learning and meta-learning. Open-source code and datasets are provided.

https://arxiv.org/abs//2401.06080

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