Imagen de Portada

Tunable MAGMAX: Preference-Aware Model Merging for Continual Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Hiroshima, Kei, Uchida, Kento, Shirakawa, Shinichi
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Surrogate Benchmarks for Model Merging Optimization
por: Akizuki, Rio, et al.
Publicado: (2025)

Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
por: Ashizawa, Rin, et al.
Publicado: (2025)

GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
por: Kawaharazuka, Kento, et al.
Publicado: (2024)

Test-Time Augmentation for Traveling Salesperson Problem
por: Ishiyama, Ryo, et al.
Publicado: (2024)

Robotic Constrained Imitation Learning for the Peg Transfer Task in Fundamentals of Laparoscopic Surgery
por: Kawaharazuka, Kento, et al.
Publicado: (2024)

BD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning
por: Xie, Yuhan, et al.
Publicado: (2026)

PLATE: Plasticity-Tunable Efficient Adapters for Geometry-Aware Continual Learning
por: Cosentino, Romain
Publicado: (2026)

Sparsity-Aware Evolution for Model Merging
por: Zhang, Huan, et al.
Publicado: (2026)

Unlocking the Potential of Continual Model Merging: An ODE Perspective
por: Lin, Lihong, et al.
Publicado: (2026)

Toward a Holistic Approach to Continual Model Merging
por: Phan, Hoang, et al.
Publicado: (2025)

CALM: Consensus-Aware Localized Merging for Multi-Task Learning
por: Yan, Kunda, et al.
Publicado: (2025)

CABS: Conflict-Aware and Balanced Sparsification for Enhancing Model Merging
por: Yang, Zongzhen, et al.
Publicado: (2025)

MIN-Merging: Merge the Important Neurons for Model Merging
por: Liang, Yunfei
Publicado: (2025)

K-Merge: Online Continual Merging of Adapters for On-device Large Language Models
por: Shenaj, Donald, et al.
Publicado: (2025)

Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs
por: Zhang, Dingkun, et al.
Publicado: (2025)

Symmetry-Aware Graph Metanetwork Autoencoders: Model Merging through Parameter Canonicalization
por: Boufalis, Odysseas, et al.
Publicado: (2025)

Train with Perturbation, Infer after Merging: A Two-Stage Framework for Continual Learning
por: Qiu, Haomiao, et al.
Publicado: (2025)

MAny: Merge Anything for Multimodal Continual Instruction Tuning
por: Gao, Zijian, et al.
Publicado: (2026)

Bayesian Model Merging
por: Li, Kaiyang, et al.
Publicado: (2026)

FW-Merging: Scaling Model Merging with Frank-Wolfe Optimization
por: Chen, Hao Mark, et al.
Publicado: (2025)

PSO-Merging: Merging Models Based on Particle Swarm Optimization
por: Zhang, Kehao, et al.
Publicado: (2025)

SimMerge: Learning to Select Merge Operators from Similarity Signals
por: Bolton, Oliver, et al.
Publicado: (2026)

MagMax: Leveraging Model Merging for Seamless Continual Learning
por: Marczak, Daniel, et al.
Publicado: (2024)

Modular Delta Merging with Orthogonal Constraints: A Scalable Framework for Continual and Reversible Model Composition
por: Khan, Haris, et al.
Publicado: (2025)

Continual Learning in Vision-Language Models via Aligned Model Merging
por: Sokar, Ghada, et al.
Publicado: (2025)

Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
por: Zhang, Bingjie, et al.
Publicado: (2025)

Decom-Renorm-Merge: Model Merging on the Right Space Improves Multitasking
por: Chaichana, Yuatyong, et al.
Publicado: (2025)

COPR: Continual Human Preference Learning via Optimal Policy Regularization
por: Zhang, Han, et al.
Publicado: (2024)

Sample-Efficient Preference-based Reinforcement Learning with Dynamics Aware Rewards
por: Metcalf, Katherine, et al.
Publicado: (2024)

Model Merging: Foundations and Algorithms
por: Crisostomi, Donato
Publicado: (2026)

Model Merging in the Essential Subspace
por: Li, Longhua, et al.
Publicado: (2026)

Machine Learning-Based Self-Localization Using Internal Sensors for Automating Bulldozers
por: Sawafuji, Hikaru, et al.
Publicado: (2025)

Continuous-Utility Direct Preference Optimization
por: Mohsin, Muhammad Ahmed, et al.
Publicado: (2026)

CAT Merging: A Training-Free Approach for Resolving Conflicts in Model Merging
por: Sun, Wenju, et al.
Publicado: (2025)

Pareto Continual Learning: Preference-Conditioned Learning and Adaption for Dynamic Stability-Plasticity Trade-off
por: Lai, Song, et al.
Publicado: (2025)

MergeMix: Optimizing Mid-Training Data Mixtures via Learnable Model Merging
por: Wang, Jiapeng, et al.
Publicado: (2026)

Clear Preferences Leave Traces: Reference Model-Guided Sampling for Preference Learning
por: Diwan, Nirav, et al.
Publicado: (2025)

Training-free Heterogeneous Model Merging
por: Xu, Zhengqi, et al.
Publicado: (2024)

Multi-Level Collaboration in Model Merging
por: Li, Qi, et al.
Publicado: (2025)

Revisiting Weight Averaging for Model Merging
por: Choi, Jiho, et al.
Publicado: (2024)