Author

V. Bonagiri

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Preprint Aug 2026

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.

Utkarsh Agarwal, V. Bonagiri, Raul Astudillo et al. · 0 citations