arXiv:2509.02555v2 Announce Type: replace-cross Abstract: Model merging techniques aim to integrate the abilities of multiple models into a single model. Most model merging techniques have hyperparameters, and their setting affects the performance of the merged model. Because several existing works show that tuning hyperparameters in model merging can enhance the merging outcome, developing hyperparameter optimization algorithms for model merging is a promising direction. However, its optimization process is computationally expensive, particularly in merging LLMs. In this work, we develop surr
The rapid development and proliferation of large language models (LLMs) and other complex AI architectures create an immediate need for efficient optimization techniques, particularly concerning hyperparameter tuning for merging models.
Efficient model merging can significantly reduce the computational cost and improve the performance of complex AI systems, directly impacting the accessibility and practical application of advanced AI.
The ability to more effectively merge AI models reduces the barrier to combining specialized AI capabilities, potentially leading to more versatile and powerful AI without proportional increases in underlying model size.
- · AI developers
- · Cloud computing providers (reduced resource demand)
- · Enterprises adopting AI
- · AI researchers
- · Companies relying on monolithic, untuned AI models
Optimization techniques for model merging lead to more performant and resource-efficient AI models.
Reduced computational expense for model fusion allows for faster iteration and development of advanced AI applications.
More efficient and powerful AI models could accelerate the development of autonomous agentic systems and other complex AI architectures.
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Read at arXiv cs.AI