ALTO: Adaptive LoRA Tuning and Orchestration for Heterogeneous LoRA Training Workloads
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 sha...
| Authors | Jingwei Zuo, Xinze Feng, Zien Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 April 2026 |
| Last updated at source | 10 April 2026 |
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