arXiv AI August 21, 2026 By Anke Tang, Li Shen, Yong Luo, Shiwei Liu, Han Hu, Bo Du, Dacheng Tao

Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion

arXiv:2406. 09770v2 Announce Type: replace-cross Abstract: Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost of training and evaluating models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv Machine Learning

Jun 16

Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

arXiv:2606. 15115v1 Announce Type: new Abstract: Multi-objective optimization (MOO) has emerged as a powerful approach to solving complex optimization problems involving multiple objectives.

By Yiyi Zhu, Yaolin Wen, Xiang Xia, Xin An, Hanyi Si, Xiang Shu, Yangde Fu, Liang Dou, Hong Qian

benchmarks safety

arXiv Machine Learning

Jun 19

Interactive Pareto navigation for deep multi-task learning

arXiv:2606. 19521v1 Announce Type: new Abstract: In multi-task learning, handling an increasing number of objectives can quickly become challenging, both in terms of the computational resources and the decision maker's capacity to choose appropriate trade-offs.

By Augustina C. Amakor, Konstantin Sonntag, Sebastian Peitz

arXiv Machine Learning

Jun 3

CoAction: Cross-task Correlation-aware Pareto Set Learning

arXiv:2605. 01712v2 Announce Type: replace Abstract: Pareto set learning (PSL) is an emerging paradigm in multi-objective optimization that trains neural networks to map preference vectors to Pareto optimal solutions.

By Xinyue Chen, Yingxuan Liang, Yiqin Huang, Chikai Shang, Hai-Lin Liu, Fangqing Gu

llms rag efficiency benchmarks

arXiv AI

Jun 2

DOT-MoE: Differentiable Optimal Transport for MoEfication

arXiv:2606. 01666v1 Announce Type: cross Abstract: The scaling of Large Language Models (LLMs) has driven significant performance gains but created substantial challenges in inference efficiency.

By Udbhav Bamba, Arnav Chavan, Aryamaan Thakur, Steve Teig, Deepak Gupta

llms efficiency benchmarks

arXiv AI

Aug 21

Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts

arXiv:2608. 20061v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) architectures significantly expand model capacity without a proportional increase in computational cost.

By Nayeon Kim, Hojin Lee, Yunju Bak, Jaesun Park, Boseop Kim

benchmarks

arXiv Machine Learning

3d ago

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models

arXiv:2604.13287v2 Announce Type: replace Abstract: Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre...

By Gabriel Afriat, Xiang Meng, Shibal Ibrahim, Hussein Hazimeh, Rahul Mazumder

llms efficiency benchmarks

One email a morning, machine-written

Related stories

Diversity-Driven Offline Multi-Objective Optimization via Nested Pareto Set Learning

Interactive Pareto navigation for deep multi-task learning

CoAction: Cross-task Correlation-aware Pareto Set Learning

DOT-MoE: Differentiable Optimal Transport for MoEfication

Let's Scale Step by Step: Compute-Efficient Hyperparameter Transfer for Large-Scale Mixture-of-Experts

MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models