Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts

• URL: http://arxiv.org/abs/2507.07140v2
• Date: Mon, 14 Jul 2025 04:57:05 GMT
• Title: Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts
• Authors: Samin Yeasar Arnob, Zhan Su, Minseon Kim, Oleksiy Ostapenko, Riyasat Ohib, Esra'a Saleh, Doina Precup, Lucas Caccia, Alessandro Sordoni,
• Abstract summary: We study the properties of sparse adapters, which train only a subset of weights in the base neural network, as potential building blocks of modular architectures.<n>First, we propose a simple method for training highly effective sparse adapters, which is conceptually simpler than existing methods in the literature.<n>Next, we investigate the merging properties of these sparse adapters by merging adapters for up to 20 natural language processing tasks.
• Score: 72.22148263683037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Merging parameter-efficient task experts has recently gained growing attention as a way to build modular architectures that can be rapidly adapted on the fly for specific downstream tasks, without requiring additional fine-tuning. Typically, LoRA serves as the foundational building block of such parameter-efficient modular architectures, leveraging low-rank weight structures to reduce the number of trainable parameters. In this paper, we study the properties of sparse adapters, which train only a subset of weights in the base neural network, as potential building blocks of modular architectures. First, we propose a simple method for training highly effective sparse adapters, which is conceptually simpler than existing methods in the literature and surprisingly outperforms both LoRA and full fine-tuning in our setting. Next, we investigate the merging properties of these sparse adapters by merging adapters for up to 20 natural language processing tasks, thus scaling beyond what is usually studied in the literature. Our findings demonstrate that sparse adapters yield superior in-distribution performance post-merging compared to LoRA or full model merging. Achieving strong held-out performance remains a challenge for all methods considered.

Related papers

• MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models [61.89384981175277]
  We propose a emphheterogeneous textbfMixture-of-Adapters (MoA) approach to integrate Low-Rank Adaptation (LoRA) and Mixture-of-Experts (MoE)<n> Experimental results demonstrate that heterogeneous MoA outperforms homogeneous MoE-LoRA methods in both performance and parameter efficiency.
  arXiv  Detail & Related papers  (2025-06-06T09:54:19Z)
• PointLoRA: Low-Rank Adaptation with Token Selection for Point Cloud Learning [54.99373314906667]
  Self-supervised representation learning for point cloud has demonstrated effectiveness in improving pre-trained model performance across diverse tasks.<n>As pre-trained models grow in complexity, fully fine-tuning them for downstream applications demands substantial computational and storage resources.<n>We propose PointLoRA, a simple yet effective method that combines low-rank adaptation (LoRA) with multi-scale token selection to efficiently fine-tune point cloud models.
  arXiv  Detail & Related papers  (2025-04-22T16:41:21Z)
• Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment [20.382810396966473]
  Low-Rank Adaptation (LoRA) enables parameter-efficient fine-tuning for Large Language Models (LLMs)<n>Current methods optimize LoRA by initializing with static singular value decomposition subsets, leading to suboptimal leveraging of pre-trained knowledge.<n>We propose underlineGreat LunderlineoRunderlineA Mixture-of-Experunderlinet (GOAT)<n>GOAT integrates relevant priors using an SVD-structured MoE, and aligns optimization with full fine-tuned MoE by deriving a theoretical scaling factor
  arXiv  Detail & Related papers  (2025-02-24T06:48:13Z)
• Rank Also Matters: Hierarchical Configuration for Mixture of Adapter Experts in LLM Fine-Tuning [5.074620301447097]
  We propose a hierarchical scheme for expert allocation and rank configuration, HILO, for large language models (LLMs)<n>HILO dynamically adjusts the number and rank of adapter experts across layers, matching the varying representational complexity of model layers in adapter-granularity.<n>Experiments on multiple benchmark tasks demonstrate that HILO outperforms existing methods in accuracy while introducing fewer trainable parameters.
  arXiv  Detail & Related papers  (2025-02-06T08:58:03Z)
• RECAST: Reparameterized, Compact weight Adaptation for Sequential Tasks [16.512587987753967]
  RECAST is a novel method that dramatically reduces task-specific trainable parameters to fewer than 50.<n>We show that RECAST outperforms the state-of-the-art by up to 3% across various scales, architectures, and parameter spaces.
  arXiv  Detail & Related papers  (2024-11-25T19:08:38Z)
• Less is More: Extreme Gradient Boost Rank-1 Adaption for Efficient Finetuning of LLMs [75.11449420928139]
  Fine-tuning Large Language Models (LLMs) has become a crucial technique for adapting pre-trained models to downstream tasks. Low-Rank Adaptation (LoRA) has emerged as a promising solution, but there exists a gap between the practical performance of low-rank adaptations and its theoretical optimum. We propose eXtreme Gradient Boosting LoRA, a novel framework that bridges this gap by leveraging the power of ensemble learning.
  arXiv  Detail & Related papers  (2024-10-25T17:07:13Z)
• LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration [62.3751291442432]
  We propose LoRA-IR, a flexible framework that dynamically leverages compact low-rank experts to facilitate efficient all-in-one image restoration. LoRA-IR consists of two training stages: degradation-guided pre-training and parameter-efficient fine-tuning. Experiments demonstrate that LoRA-IR achieves SOTA performance across 14 IR tasks and 29 benchmarks, while maintaining computational efficiency.
  arXiv  Detail & Related papers  (2024-10-20T13:00:24Z)
• Search for Efficient Large Language Models [52.98684997131108]
  Large Language Models (LLMs) have long held sway in the realms of artificial intelligence research. Weight pruning, quantization, and distillation have been embraced to compress LLMs, targeting memory reduction and inference acceleration. Most model compression techniques concentrate on weight optimization, overlooking the exploration of optimal architectures.
  arXiv  Detail & Related papers  (2024-09-25T21:32:12Z)
• Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs [1.5503410315996757]
  Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks. However, the ever-growing complexity of LLMs demands immense computational resources. This paper introduces Train Low-Rank Approximation (TT-LoRA), a novel parameter-efficient fine-tuning (PEFT) approach.
  arXiv  Detail & Related papers  (2024-08-02T04:45:58Z)
• One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning [34.109808214968176]
  Generalized LoRA (GLoRA) is an advanced approach for universal parameter-efficient fine-tuning tasks. It employs a generalized prompt module to optimize pre-trained model weights and adjust intermediate activations. GLoRA exhibits strong transfer learning, few-shot learning and domain generalization abilities.
  arXiv  Detail & Related papers  (2023-06-13T17:59:32Z)
• AdaMix: Mixture-of-Adapter for Parameter-efficient Tuning of Large Language Models [119.7093605087114]
  Fine-tuning large-scale pre-trained language models to downstream tasks require updating hundreds of millions of parameters. This not only increases the serving cost to store a large copy of the model weights for every task, but also exhibits instability during few-shot task adaptation. We introduce a new mechanism to improve adapter capacity without increasing parameters or computational cost by two key techniques.
  arXiv  Detail & Related papers  (2022-05-24T23:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.