Title: Alignment from Ranking and Rating Information
Primary area: reinforcement learning
Keywords: direct preference optimization, alignment, sample complexity guarantees

Abstract: The class of direct preference optimization (DPO) algorithms has emerged as a
promising approach for solving the alignment problem in foundation models. These
algorithms work with very limited feedback in the form of pairwise preferences
and fine-tune models to align with these preferences without explicitly learning a
reward model. While the form of feedback used by these algorithms makes the
data collection process easy, its ambiguity in terms of the quality of responses has
significant negative implications, including incentivizing policies that favor out-of-
distribution responses, a phenomenon referred to as likelihood displacement. In this
paper, we study how DPO-style algorithms can leverage additional information in
the form of rating gap, which informs the learner how much the preferred response
is better than the rejected one. We present new algorithms that can achieve faster
statistical rates than DPO in presence of accurate rating gap information. Moreover,
we theoretically prove and empirically show that the performance of our algorithms
is robust to inaccuracy in rating gaps. Finally, we demonstrate the solid performance
of our algorithms in comparison to a number of DPO-style algorithms across a
wide range of LLMs and evaluation benchmarks.