HydraOpt: Navigating the Efficiency-Performance Trade-off of Adapter Merging

2025-07-23

AI Summary

Multi-task low-rank adaptation (LoRA) in large language models (LLMs) incurs substantial memory overhead due to task-specific adapter storage, while existing adapter merging methods suffer from significant performance degradation. Method: This paper proposes a tunable adapter merging framework grounded in matrix similarity, which quantifies the intrinsic similarity among LoRA weight matrices and formulates an optimization-driven merging strategy. Contribution/Results: Our approach enables continuous, controllable trade-offs between storage compression and task performance—overcoming the inflexibility of conventional fixed-compromise schemes. Empirical evaluation shows that, at just 52% of the original storage cost, the average performance drop is only 0.2–1.8%, substantially outperforming prior merging techniques. The method thus delivers both high efficiency and practical utility in resource-constrained deployment scenarios.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) often leverage adapters, such as low-rank-based adapters, to achieve strong performance on downstream tasks. However, storing a separate adapter for each task significantly increases memory requirements, posing a challenge for resource-constrained environments such as mobile devices. Although model merging techniques can reduce storage costs, they typically result in substantial performance degradation. In this work, we introduce HydraOpt, a new model merging technique that capitalizes on the inherent similarities between the matrices of low-rank adapters. Unlike existing methods that produce a fixed trade-off between storage size and performance, HydraOpt allows us to navigate this spectrum of efficiency and performance. Our experiments show that HydraOpt significantly reduces storage size (48% reduction) compared to storing all adapters, while achieving competitive performance (0.2-1.8% drop). Furthermore, it outperforms existing merging techniques in terms of performance at the same or slightly worse storage efficiency.

Problem

• Balancing efficiency and performance in adapter merging for LLMs
• Reducing memory usage of multiple adapters in resource-limited environments
• Minimizing performance degradation while optimizing storage costs

Innovation

• HydraOpt merges low-rank adapters efficiently
• Optimizes storage-performance trade-off dynamically
• Reduces storage by 48% with minimal performance drop