@inproceedings{
lu2024discovering,
title={Discovering Preference Optimization Algorithms with and for Large Language Models},
author={Chris Lu and Samuel Holt and Claudio Fanconi and Alex James Chan and Jakob Nicolaus Foerster and Mihaela van der Schaar and Robert Tjarko Lange},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=erjQDJ0z9L}
}
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.
Discovering Preference Optimization Algorithms with and for Large Language Models
Chris Lu Samuel Holt Claudio Fanconi Alex James Chan Jakob Nicolaus Foerster Mihaela van der Schaar Robert Tjarko Lange