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Researchers developed Reweighting Enhanced task Singular Merging (RESM), a model merging technique that consistently outperforms data mixture strategies for balancing Helpfulness, Honesty, and Harmlessness (3H) in large language models. RESM improves 3H alignment by 1-3% over other merging methods and 2-5% over data mixture approaches by addressing preference noise and adapting to layer-specific sparsity.