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October 10, 2025Open Access

DoRAN: Stabilizing Weight-Decomposed Low-Rank Adaptation via Noise Injection and Auxiliary Networks

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DoRAN demonstrates improved training stability and sample efficiency in PEFT methods, indicating effective noise injection and auxiliary networks.

DoRAN outperforms traditional low-rank adaptation methods, enhancing training stability and sample efficiency.
Comprehensive experiments show consistent improvements in vision and language benchmarks, with specific metrics highlighting efficacy.
The method incorporates noise injection to stabilize training and dynamically generates low-rank matrices through auxiliary networks.
This approach points to future directions for more robust and efficient fine-tuning of foundation models.

Diep et al. (2025) studied this question.

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DoRAN demonstrates improved training stability and sample efficiency in PEFT methods, indicating effective noise injection and auxiliary networks.