LoRA+: Efficient Low Rank Adaptation of Large Models

• URL: http://arxiv.org/abs/2402.12354v2
• Date: Thu, 4 Jul 2024 18:33:00 GMT
• Title: LoRA+: Efficient Low Rank Adaptation of Large Models
• Authors: Soufiane Hayou, Nikhil Ghosh, Bin Yu,
• Abstract summary: We show that Low Rank Adaptation (LoRA) leads to suboptimal finetuning of models with large width (embedding dimension) We then show that this suboptimality of LoRA can be corrected simply by setting different learning rates for the LoRA adapter matrices A and B with a well-chosen ratio. In our experiments, LoRA$+$ improves performance (1-2 $%$ improvements) and finetuning speed (up to $sim$ 2X SpeedUp) at the same computational cost as LoRA.
• Score: 13.074320303580361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we show that Low Rank Adaptation (LoRA) as originally introduced in Hu et al. (2021) leads to suboptimal finetuning of models with large width (embedding dimension). This is due to the fact that adapter matrices A and B in LoRA are updated with the same learning rate. Using scaling arguments for large width networks, we demonstrate that using the same learning rate for A and B does not allow efficient feature learning. We then show that this suboptimality of LoRA can be corrected simply by setting different learning rates for the LoRA adapter matrices A and B with a well-chosen ratio. We call this proposed algorithm LoRA$+$. In our extensive experiments, LoRA$+$ improves performance (1-2 $\%$ improvements) and finetuning speed (up to $\sim$ 2X SpeedUp), at the same computational cost as LoRA.

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