[2604.05426] ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads

Computer Science > Machine Learning arXiv:2604.05426 (cs) [Submitted on 7 Apr 2026] Title:ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads Authors:Jingwei Zuo, Xinze Feng, Zien Liu, Kaijian Wang, Fanjiang Ye, Ye Cao, Zhuang Wang, Yuke Wang View a PDF of the paper titled ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads, by Jingwei Zuo and 7 other authors View PDF HTML (experimental) Abstract:Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quality adapter often requires systematic hyperparameter tuning because LoRA performance is highly sensitive to configuration choices. In practice, this leads to many concurrent LoRA jobs, often spanning heterogeneous tasks in multi-tenant environments. Existing systems largely handle these jobs independently, which both wastes computation on weak candidates and leaves GPUs underutilized. We present ALTO (Adaptive LoRA Tuning and Orchestration), a co-designed training system that accelerates LoRA hyperparameter tuning while enabling efficient cluster sharing across heterogeneous tasks. The central insight behind ALTO is that when multiple tuning jobs run concurrently over a shared frozen backbone, they expose optimization opportunities that single-job designs cannot exploit. Building on this, ALTO monitors loss trajectories to terminate unpromising configurations early, uses fu...