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In this paper, we reveal that the scaling factor $\\alpha$ and the learning rate function differently, with $\\alpha$ emerging as the dominant driver of effective optimization, delivering gains that cannot be replicated by learning rate scaling alone. Through the synergy of extensive empirical analysis and a theoretical Signal-Drift framework, we uncover three findings into LoRA's scaling mechanism: First, LoRA's spectral suppression smooths the optimization landscape, rendering standard hyperparameters overly conservative and creating an optimization gap. Second, when leveraging this smoothness to accelerate convergence, $\\alpha$ outperforms the learning rate by amplifying the task signal without increasing the drift ratio. Third, the optimal scaling factor follows a","title":"The Hidden Power of Scaling Factor in LoRA Optimization","url":"https://arxiv.org/abs/2606.12883","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.12883v1 Announce Type: new \nAbstract: In Low-Rank Adaptation (LoRA), the scaling factor $\\alpha$ is often treated as a mere complement to the learning rate, yet its role in optimization remains poorly understood. In this paper, we reveal that the scaling factor $\\alpha$ and the learning rate function differently, with $\\alpha$ emerging as the dominant driver of effective optimization, delivering gains that cannot be replicated by learning rate scaling alone. Through the synergy of extensive empirical analysis and a theoretical Signal-Drift framework, we uncover three findings into LoRA's scaling mechanism: First, LoRA's spectral suppression smooths the optimization landscape, rendering standard hyperparameters overly conservative and creating an optimization gap. Second, when leveraging this smoothness to accelerate convergence, $\\alpha$ outperforms the learning rate by amplifying the task signal without increasing the drift ratio. 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