A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models

• URL: http://arxiv.org/abs/2502.15828v1
• Date: Thu, 20 Feb 2025 05:58:53 GMT
• Title: A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models
• Authors: Mengyang Sun, Yihao Wang, Tao Feng, Dan Zhang, Yifan Zhu, Jie Tang,
• Abstract summary: Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domain adaptation.<n>To address the limited expressive capacity of LoRA, the Mixture-of-Expert (MoE) has been introduced for incorporating multiple LoRA adapters.<n>We propose a new training strategy for MoE-LoRA, to stabilize and boost its feature learning procedure by multi-space projections.
• Score: 22.457766373989365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In order to streamline the fine-tuning of foundation models, Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domain adaptation. The underlying concept of LoRA involves decomposing a full-rank matrix into the product of two lower-rank matrices, which reduces storage consumption and accelerates the training process. Furthermore, to address the limited expressive capacity of LoRA, the Mixture-of-Expert (MoE) has been introduced for incorporating multiple LoRA adapters. The integration of LoRA experts leads to a visible improvement across several downstream scenes. However, the mixture of LoRAs (MoE-LoRA) still exhibits its low robustness during tuning and inferring. Inspired by the Riemannian Preconditioners which train LoRA as a sub-space projector, we propose a new training strategy for MoE-LoRA, to stabilize and boost its feature learning procedure by multi-space projections. Examinations on SGD and AdamW optimizers demonstrate the effectiveness of our methodology. Source code is available at https://github.com/THUDM/MoELoRA_Riemannian.

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