arXiv:2602.05536v2 Announce Type: replace Abstract: Model merging combines multiple fine-tuned models into a single model by adding their weight updates, providing a lightweight alternative to retraining. Existing methods primarily target resolving conflicts between task updates, leaving the failure mode of over-counting shared knowledge unaddressed. We show that when tasks share aligned spectral directions (i.e., overlapping singular vectors), a simple linear combination repeatedly accumulates these directions, inflating the singular values and biasing the merged model toward shared subspaces
The proliferation of fine-tuned models and the desire for more efficient AI deployment drive research into model merging, making the identification of failure modes particularly timely.
Understanding the 'spectral over-accumulation' problem in model merging is crucial for developing robust and efficient AI systems, impacting training costs, model performance, and resource utilization.
This research highlights a fundamental limitation in current model merging techniques, suggesting a need for more sophisticated approaches beyond simple linear combinations to avoid performance degradation.
- · AI researchers focusing on model optimization
- · Organizations developing advanced model merging algorithms
- · Users of AI who benefit from more efficient model deployment
- · Developers relying on simplistic model merging techniques
- · AI applications susceptible to biased or inflated model performance
Further research into advanced, conflict-aware model merging techniques will accelerate.
New architectural designs or training methodologies may emerge that inherently mitigate shared knowledge over-accumulation.
The overall efficiency and deployment scalability of large-scale AI systems could significantly improve, reducing the compute and energy footprint.
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Read at arXiv cs.LG