arXiv:2608. 14264v1 Announce Type: cross Abstract: Model merging combines trained models directly in weight space, offering a compute-efficient alternative to additional fine-tuning.
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arXiv:2509. 02555v2 Announce Type: replace-cross Abstract: Model merging techniques aim to integrate the abilities of multiple models into a single model.
Mixture-Trained Merging (MTM) is a method for creating unified language models that combine multiple objectives—such as mathematics, code, instruction following, and controllable thinking—into a single parameter set. Instead of sequentially post‑training on each objective, MTM trains each branch on a mixture of objectives, ensuring that the branches remain compatible in weight space and can be merged without degrading performance. The approach iteratively refines merge coefficients using low‑cost evaluations and multi‑objective Bayesian optimization, outperforming naive merging and preserving distinct behaviors across domains.
arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.
arXiv:2603. 29730v2 Announce Type: replace-cross Abstract: We present mlr3mbo, a modular toolbox for Bayesian optimization in R.
tidyHEBO is a BoTorch-native Bayesian optimization tool that jointly applies Yeo-Johnson output warping to a Gaussian‑process surrogate, evaluates acquisition functions on the original objective scale, and conducts constrained cumulative Pareto search across multiple acquisition criteria. Using only default settings, it outperformed other methods on the Olympus benchmark and performed strongly on synthetic, Needle‑in‑a‑Haystack, and Bayesmark tasks, while adaptive batching offered a trade‑off between parallelization and optimization quality. These results position tidyHEBO as a robust, reproducible optimizer suitable for diverse practical problems, including scientific applications and hyperparameter tuning.
arXiv:2609.13396v1 Announce Type: new Abstract: Multi-objective Bayesian optimisation (MOBO) is a sample-efficient approach for optimising expensive black-box functions with multiple objectives. In M...