Meta-Learning Objectives for Preference Optimization

Patrick Rebeschini (University of Oxford) · Jakob Foerster (University of Oxford) · Yee Whye Teh (University of Oxford) · Carlo Alfano (Amazon/University of Oxford) · Silvia Sapora (University of Oxford)

algorithm discoveryalgorithm specializationbaseline comparison benchmark evaluationdiagnostic suiteevolutionary strategiesllm alignment mirror descentmirror preference optimizationmixed-quality datamujoco tasksnoisy data performance metrics preference optimization systematic evaluationtargeted settings

Evaluating preference optimization (PO) algorithms on LLM alignment is a challenging task that presents prohibitive costs, noise, and several variables like model size and hyper-parameters. In this work, we show that it is possible to gain insights on the efficacy of PO algorithm on much simpler benchmarks. We design a diagnostic suite of MuJoCo tasks and datasets, which we use to systematically evaluate PO algorithms, establishing a more controlled and cheaper benchmark. We then propose a novel family of PO algorithms based on mirror descent, which we call Mirror Preference Optimization (MPO). Through evolutionary strategies, we search this class to discover algorithms specialized to specific properties of preference datasets, such as mixed-quality or noisy data. We demonstrate that our discovered PO algorithms outperform all known algorithms in the targeted MuJoCo settings. Finally, based on the insights gained from our MuJoCo experiments, we design a novel PO algorithm that significantly outperforms existing baselines in an LLM alignment task.