Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions

2026-03-10 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Advanced, quick

Summary

A new survey introduces the FUSE taxonomy, a four-dimensional framework for understanding model merging in the context of large language models (LLMs). This framework organizes merging techniques by Foundations, Unification Strategies, Scenarios, and Ecosystem. The survey details theoretical underpinnings such as loss landscape geometry and mode connectivity, including the linear mode connectivity hypothesis. It systematically reviews algorithmic approaches like weight averaging, task vector arithmetic, sparsification-enhanced methods, Mixture-of-Experts architectures, and evolutionary optimization. Furthermore, it explores applications in multi-task learning, safety alignment, domain specialization, multilingual transfer, and federated learning. The survey also covers the supporting ecosystem of open-source tools, community platforms, and evaluation benchmarks, while identifying challenges like theoretical gaps and scalability barriers.

Key takeaway

For AI Scientists and NLP Engineers developing or deploying LLMs, understanding model merging techniques is crucial for efficient capability composition. You should explore methods like weight averaging or task vector arithmetic to combine specialized models without the computational cost of full retraining or ensembles. This approach can significantly reduce resource expenditure while enhancing model versatility across various applications, from multilingual transfer to federated learning.

Key insights

Model merging offers a computationally efficient way to combine LLM capabilities without extensive retraining.

Principles

• Loss landscape geometry influences merging success.
• Mode connectivity enables combining models.
• Merging is an alternative to ensembles.

Method

The FUSE taxonomy (Foundations, Unification Strategies, Scenarios, Ecosystem) provides a structured approach to analyze and advance model merging techniques, including weight averaging and task vector arithmetic.

In practice

• Apply merging for multi-task learning.
• Use merging for safety alignment.
• Implement for domain specialization.

Topics

• Model Merging
• Large Language Models
• Loss Landscape Geometry
• Mixture-of-Experts
• Federated Learning

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Researcher, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.