Discovering Preference Optimization Algorithms with and for Large
  Language Models
Authors: name: Chris Lu, name: Samuel Holt, name: Claudio Fanconi, name: Alex J. Chan, name: Jakob Foerster, name: Mihaela van der Schaar, name: Robert Tjarko Lange

Offline preference optimization is a key method for enhancing and controlling
the quality of Large Language Model (LLM) outputs. Typically, preference
optimization is approached as an offline supervised learning task using
manually-crafted convex loss functions. While these methods are based on
theoretical insights, they are inherently constrained by human creativity, so
the large search space of possible loss functions remains under explored. We
address this by performing LLM-driven objective discovery to automatically
discover new state-of-the-art preference optimization algorithms without
(expert) human intervention. Specifically, we iteratively prompt an LLM to
propose and implement new preference optimization loss functions based on
previously-evaluated performance metrics. This process leads to the discovery
of previously-unknown and performant preference optimization algorithms. The
best performing of these we call Discovered Preference Optimization (DiscoPOP),
a novel algorithm that adaptively blends logistic and exponential losses.
Experiments demonstrate the state-of-the-art performance of DiscoPOP and its
successful transfer to held-out tasks.