FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts

• URL: http://arxiv.org/abs/2510.08396v2
• Date: Thu, 23 Oct 2025 17:14:06 GMT
• Title: FlyLoRA: Boosting Task Decoupling and Parameter Efficiency via Implicit Rank-Wise Mixture-of-Experts
• Authors: Heming Zou, Yunliang Zang, Wutong Xu, Yao Zhu, Xiangyang Ji,
• Abstract summary: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for foundation models.<n>MoE-based LoRA variants show promise in mitigating intra-task correlations in single-task instruction tuning.<n>FlyLoRA is an implicit MoE-based LoRA variant that introduces rank-wise expert activation in the up-projection matrix.
• Score: 44.21416999726094
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Low-Rank Adaptation (LoRA) is a widely used parameter-efficient fine-tuning method for foundation models, but it suffers from parameter interference, resulting in suboptimal performance. Although Mixture-of-Experts (MoE)-based LoRA variants show promise in mitigating intra-task correlations in single-task instruction tuning, they introduce additional router parameters and remain ineffective in multi-task model merging where inter-task interference arises. Inspired by the fly olfactory circuit, we propose FlyLoRA, an implicit MoE-based LoRA variant that introduces: (1) rank-wise expert activation in the up-projection matrix, and (2) an implicit router that unifies expert routing and down-projection, where a frozen sparse random projection matrix replaces the traditional dense trainable version. This design resolves the trade-off between intra-task decorrelation and computational efficiency by eliminating the need for an explicit router, while inherently mitigating inter-task interference due to the orthogonality property of random matrices. Extensive experiments across four domains -- general knowledge understanding, scientific question answering, mathematical reasoning, and code generation -- demonstrate consistent performance improvements over existing methods. Beyond empirical gains, FlyLoRA highlights how biological structures can inspire innovations in AI technologies. Code is available at https://github.com/gfyddha/FlyLoRA.

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