Practitioners commonly align large language models using pairwise preferences, i.e., given labels of the type response A is preferred to response B for a given input.
Perhaps less commonly, methods have also been developed for binary feedback,
i.e. training models given labels of type response A is good or bad. We show how an existing performant binary feedback method, the Cringe Loss, can be generalized
to the pairwise preference setting using a simple soft margin extension.
Pairwise Cringe Loss is straightforward to
implement and efficient to train, and we find it outperforms state-of-the-art preference optimization algorithms such as PPO and DPO on the AlpacaFarm benchmark.