AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.
Revisiting model interpolation for efficient reasoning.arXiv preprint arXiv:2510.10977
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The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.
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Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
The paper introduces a new taxonomy for model merging methods and reviews their applications in LLMs, MLLMs, continual learning, multi-task learning, and other subfields while outlining open challenges.