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

large-language-models › model-architecture

📄 Abstract

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.

Authors (2)

Shihao Ji

Zihui Song

Submitted

October 19, 2025

arXiv Category

cs.LG

arXiv PDF

Key Contributions

Introduces L-MoE, a novel framework that unifies Mixture of Experts (MoE) and Low-Rank Adaptation (LoRA) for end-to-end trainable LLMs. L-MoE redefines experts as LoRA adapters, dynamically composed by a lightweight gating network, enabling efficient scaling and specialization.

Business Value

Facilitates the creation of highly efficient and adaptable LLMs, enabling faster fine-tuning for diverse tasks and potentially reducing the computational cost of deploying large models.

Paper Metadata

Innovation Type

Architectural

Deployment Feasibility

Feasible, building on established MoE and LoRA concepts. The end-to-end training and lightweight gating network are key innovations that could simplify deployment.

Limitations Addressed

Addresses the challenge of scaling LLMs efficiently while allowing for specialization, combining the benefits of MoE's sparse activation with LoRA's parameter efficiency.

Performance Gains

Enables end-to-end training of MoE models with LoRA experts, maintaining constant computational cost during inference while allowing for specialization.

Technical Tags

Mixture of Experts (MoE)Low-Rank Adaptation (LoRA)LLMsparameter-efficient fine-tuningend-to-end traininglightweight gating networkdynamic compositionsparse activationauto-regressive language modeling

Research Topics

Mixture of ExpertsParameter-Efficient Fine-TuningLLM ScalingModel ArchitectureSparse Models

Methods & Architectures

End-to-end training of L-MoELoRA expert compositionLightweight gating networkWeighted averaging of LoRA adaptersAuto-regressive language modeling objective Mixture of Experts (MoE)Low-Rank Adaptation (LoRA)L-MoE (Lightweight Mixture of LoRA Experts)

Applications & Tasks

Natural Language Processing Large Language Model Training Parameter-Efficient Fine-Tuning LLM ScalingEfficient Fine-TuningModel Specialization End-to-end training of MoE models using LoRADynamically composing task-specialized adapters

Related Fields

Natural Language ProcessingDeep LearningModel ArchitecturesParameter-Efficient Fine-TuningMixture of Experts

Keywords

Mixture of ExpertsMoELoRALLMsparameter-efficient fine-tuningend-to-end traininglightweight gatingsparse activationadapter learningmodel scalingauto-regressive

Academic Context

#Mixture of Experts#Parameter-Efficient Fine-Tuning#LLM Scaling#Model Architecture#Sparse Models

Commercial Potential

Potential Products

Efficient LLM training frameworksSpecialized LLM services

Target Industries

TechnologyAI ResearchCloud Computing

Use Case Examples

Training a single MoE model with specialized LoRA adapters for different domains (e.g., medical, legal).Dynamically composing adapters for real-time task adaptation in LLM applications.

Competitive Edge

Combines the benefits of MoE and LoRA in a novel, end-to-end trainable architecture, offering a more efficient and flexible approach to LLM scaling and specialization compared to traditional MoE or standalone LoRA.

Market Opportunity

Large and growing market for LLM training and fine-tuning solutions.

Revenue Models

Licensing of the L-MoE frameworkcloud-based LLM training services.

Resource Requirements

Compute Needs

Requires significant compute for training, but potentially less than dense models of comparable capacity due to sparse activation.

Data Requirements

Requires large-scale text datasets for auto-regressive language modeling.

Deployment Constraints

Complexity of managing MoE routing and adapter composition.

Scalability

Designed for scalability through MoE architecture, with efficient parameter updates via LoRA.

Production Readiness

Maturity Level

Research

Time to Market

2-3 years for integration into major LLM frameworks.

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