Papers with R**escale

1 papers
STAR: Spectral Truncation and Rescale for Model Merging (2025.naacl-short)

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Challenge: Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning.
Approach: They propose a model merging technique that aims at mitigating "merging conflicts" by truncating small components in the respective spectral spaces and then an automatic parameter rescaling scheme to retain the nuclear norm of the original matrix.
Outcome: The proposed model outperforms baseline models on flan-T5 by 4.2% and is robust to hyperparamater choice.

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