AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.
The No-U-Turn sampler: adaptively setting path lengths in hamiltonian monte carlo.Journal of Ma- chine Learning Research, 15(1):1593–1623
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AP-BMM: Approximating Capability-Cost Pareto Sets of LLMs via Asynchronous Prior-Guided Bayesian Model Merging
AP-BMM approximates Pareto sets of layer-wise merged LLMs for accuracy-cost trade-offs via prior-guided asynchronous Bayesian optimization and reranking.