An optimizer wrapper that clusters per-layer momentum and scales learning rates by cluster-wise median deviations improves perplexity and accuracy across LLM and MLLM training, and lets LoRA pretraining approach full-rank performance.
EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition
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abstract
Parameter-efficient fine-tuning methods, such as LoRA, reduces the number of trainable parameters. However, they often suffer from scalability issues and differences between their learning pattern and full fine-tuning. To overcome these limitations, we propose Efficient Weight-Decomposed Low-Rank Adaptation (EDoRA): a novel PEFT method that decomposes pre-trained weights into magnitude and directional components. By freezing low-rank matrices, initializing them by singular value decomposition, and introducing a small trainable matrix between them, EDoRA achieves substantial reduction in trainable parameters while maintaining learning capacity. Experimental results on the GLUE benchmark demonstrate that EDoRA achieves competitive or superior performance compared to state-of-the-art methods, such as LoRA and DoRA, with up to 30x fewer trainable parameters. This makes EDoRA a highly efficient solution for adapting LLMs to diverse tasks under memory-constrained settings. Code is available at https://github.com/Hamid-Nasiri/EDoRA .
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cs.LG 1years
2025 1verdicts
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Taming LLMs by Scaling Learning Rates with Gradient Grouping
An optimizer wrapper that clusters per-layer momentum and scales learning rates by cluster-wise median deviations improves perplexity and accuracy across LLM and MLLM training, and lets LoRA pretraining approach full-rank performance.