L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts

• URL: http://arxiv.org/abs/2510.17898v1
• Date: Sun, 19 Oct 2025 08:44:25 GMT
• Title: L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts
• Authors: Shihao Ji, Zihui Song,
• Abstract summary: We present L-MoE: a Lightweight Mixture of LoRA Experts.<n>L-MoE redefines MoE experts as task-specialized, low-rank adapters.<n>We present the formal mathematical framework for L-MoE.
• Score: 10.21556794551883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Mixture of Experts (MoE) architecture enables the scaling of Large Language Models (LLMs) to trillions of parameters by activating a sparse subset of weights for each input, maintaining constant computational cost during inference. Concurrently, Low-Rank Adaptation (LoRA) has emerged as a dominant technique for parameter-efficiently fine-tuning LLMs on specialized tasks. In this work, we unify these two paradigms into a novel, end-to-end trainable framework named L-MoE: a Lightweight Mixture of LoRA Experts. L-MoE redefines MoE experts not as dense feed-forward networks, but as a collection of task-specialized, low-rank adapters. A lightweight gating network, trained jointly with the experts, learns to dynamically compose these LoRA adapters by computing a weighted average of their parameters for each input token. This composition is fully differentiable, allowing gradients from a standard auto-regressive language modeling objective to flow back through the entire architecture, simultaneously refining both the expert adapters and the routing strategy. This approach creates a highly parameter-efficient MoE model that is modular by design, allows for dynamic skill composition, and is trainable from end-to-end. We present the formal mathematical framework for L-MoE, detailing the differentiable routing mechanism and the joint optimization objective, thereby providing a new path toward building more efficient, scalable, and specialized language models.

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