arXiv:2510.24561v3 Announce Type: replace Abstract: LoRA has become a widely adopted method for PEFT, and its initialization methods have attracted increasing attention. However, existing methods have notable limitations: many methods do not incorporate target-domain data, while gradient-based methods exploit data only at a shallow level by relying on one-step gradient decomposition. In this paper, we establish a theoretical framework for data-aware LoRA initialization. Starting from minimizing the expectation of the parameter discrepancy between the fine-tuned and target models, we derive an
The paper addresses current limitations in LoRA initialization methods, which are becoming increasingly critical as PEFT techniques like LoRA gain widespread adoption in AI model fine-tuning.
Improved data-aware initialization for LoRA can significantly enhance the efficiency and performance of fine-tuning large AI models, reducing computational costs and improving model quality.
The proposed theoretical framework for LoRA-DA offers a more robust and data-driven approach to LoRA initialization, moving beyond current shallow or data-agnostic methods.
- · AI developers
- · Cloud AI providers
- · Researchers in efficient AI
- · Companies deploying large AI models
- · Inefficient fine-tuning methods
- · Developers relying on suboptimal initialization techniques
More efficient and effective fine-tuning of large language models and other AI systems.
Reduced computational requirements for deploying custom AI solutions, democratizing access to powerful models.
Acceleration of AI development cycles and greater innovation in specialized AI applications due to lower resource barriers.
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Read at arXiv cs.LG