Multi-Objective Bayesian Optimization for Model Merging
Results show that multi-objective Bayesian optimization is valuable as a search layer for expressive merge parameterizations in model merging and introduce MOBO-Merge, a merge-operator agnostic framework that uses multi-objective Bayesian optimization to approximate the Pareto front under a limited evaluation budget.