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.
By SeongHyeon Kim, Chaeyun Jang, Seungyoo Lee, Jiyeon Ham, Yunju Bak, Boseop Kim, Juho Lee
arXiv:2606. 05613v1 Announce Type: new Abstract: The rapid evolution of Large Language Models (LLMs) has established cross-lingual versatility as a defining feature of modern systems.
By Long P. Hoang, Yiran Zhao, Wei Lu, Wenxuan Zhang
arXiv:2510. 01167v2 Announce Type: replace-cross Abstract: Aligning large language models to human preferences is inherently multidimensional, yet most pipelines collapse heterogeneous signals into a single objective.
By Yiran Shen, Yu Xia, Jonathan Chang, Prithviraj Ammanabrolu
arXiv:2610.23062v1 Announce Type: new
Abstract: Multi-objective optimization models are often deployed in settings where decision makers (DMs) must balance some conflicting criteria. In practice, how...
By Gokhan Ceyhan
The paper critiques current multi‑task active learning (MTL‑AL) methods that use linear scalarization of objectives like uncertainty, diversity, and gradient conflict, arguing that such combinations miss Pareto‑optimal trade‑offs. It introduces a new framework that separates acquisition into informativeness and exploration, decouples conflict scoring from candidate ranking, and uses gradient surgery during training. The authors also propose a cross‑task mutual information metric and demonstrate consistent improvements on COCO, Cityscapes, and NYUv2 datasets compared to recent baselines.
By Juho Jung, Seongjae Kang, Jinyoung Han
The paper introduces DualSFT, a one-shot algorithm that simultaneously selects a parameter mask and a data subset for fine‑tuning large language models by using a shared gradient‑interaction matrix. By casting both selections as bilevel problems under a common validation objective, the authors derive a unified scoring rule that aggregates parameter importance and data utility as column‑wise and row‑wise sums of the same matrix. Experiments on 3B‑9B LLMs show that DualSFT improves target‑task performance and stability‑plasticity trade‑offs compared to single‑axis variants and outperforms sequential hybrid baselines under matched budgets.
By Xinrui Chen, Liu Yang, Ou Wu