arXiv:2605.31484v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is the most widely adopted method for fine-tuning large language models. Notably, LoRA is inherently overparameterized: multiple pairs of low-rank factors can yield the same adapted weight matrix. We show--both theoretically and empirically--that these pairs exhibit significantly different condition numbers. As a result, converging to different loss minimizers directly impacts the convergence rate of LoRA. Building on this observation, we introduce Balanced Low-Rank Adaptation (BaLoRA), a variant of LoRA that projects i
The rapid adoption of LoRA for fine-tuning large language models necessitates continuous innovation to improve efficiency and stability, making advancements like BaLoRA timely.
Improving the convergence rate and stability of LoRA directly impacts the cost and speed of developing and deploying advanced AI models, benefiting all users of LLMs.
Fine-tuning of large language models becomes more efficient and predictable, potentially lowering computational costs and accelerating model development cycles.
- · AI developers
- · Cloud providers
- · Companies using LLMs
- · AI research institutions
- · Less efficient LoRA implementations
Faster and more cost-effective fine-tuning of large language models.
Accelerated deployment of specialized AI applications across various industries due to reduced development friction.
Enhanced accessibility to advanced AI capabilities for smaller organizations or those with limited compute resources.
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Read at arXiv cs.LG