10 papers
Reinforcement Fine-Tuning Naturally Mitigates Forgetting in Continual Post-Training
Song Lai, Haohan Zhao, Rong Feng +9
Continual post-training (CPT) is a popular and effective technique for adapting foundation models like multimodal large language models to ever-evolving downstream tasks. While exi…
Few-for-Many Personalized Federated Learning
Ping Guo, Tiantian Zhang, Xi Lin +3
Personalized Federated Learning (PFL) aims to train customized models for clients with highly heterogeneous data distributions while preserving data privacy. Existing approaches of…
FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Yiming Yao, Fei Liu, Liang Zhao +3
Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover t…
Quality-Diversity Optimization as Multi-Objective Optimization
Xi Lin, Ping Guo, Yilu Liu +2
The Quality-Diversity (QD) optimization aims to discover a collection of high-performing solutions that simultaneously exhibit diverse behaviors within a user-defined behavior spac…
CoEvo: Continual Evolution of Symbolic Solutions Using Large Language Models
Ping Guo, Qingfu Zhang, Xi Lin
The discovery of symbolic solutions -- mathematical expressions, logical rules, and algorithmic structures -- is fundamental to advancing scientific and engineering progress. Howev…
Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
Weiyu Chen, Baijiong Lin, Xiaoyuan Zhang +4
Many modern deep learning applications require balancing multiple objectives that are often conflicting. Examples include multi-task learning, fairness-aware learning, and the alig…