Mixture-of-Subspaces in Low-Rank Adaptation

• URL: http://arxiv.org/abs/2406.11909v4
• Date: Sun, 02 Mar 2025 08:40:16 GMT
• Title: Mixture-of-Subspaces in Low-Rank Adaptation
• Authors: Taiqiang Wu, Jiahao Wang, Zhe Zhao, Ngai Wong,
• Abstract summary: We introduce a subspace-inspired Low-Rank Adaptation (LoRA) method, which is computationally efficient, easy to implement, and readily applicable to large language, multimodal, and diffusion models.<n>To be more flexible, we jointly learn the mixer with the original LoRA weights, and term the method Mixture-of-Subspaces LoRA.<n>MoSLoRA consistently outperforms LoRA on tasks in different modalities, including commonsense reasoning, visual instruction tuning, and subject-driven text-to-image generation.
• Score: 19.364393031148236
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we introduce a subspace-inspired Low-Rank Adaptation (LoRA) method, which is computationally efficient, easy to implement, and readily applicable to large language, multimodal, and diffusion models. Initially, we equivalently decompose the weights of LoRA into two subspaces, and find that simply mixing them can enhance performance. To study such a phenomenon, we revisit it through a fine-grained subspace lens, showing that such modification is equivalent to employing a fixed mixer to fuse the subspaces. To be more flexible, we jointly learn the mixer with the original LoRA weights, and term the method Mixture-of-Subspaces LoRA (MoSLoRA). MoSLoRA consistently outperforms LoRA on tasks in different modalities, including commonsense reasoning, visual instruction tuning, and subject-driven text-to-image generation, demonstrating its effectiveness and robustness. Codes are available at https://github.com/wutaiqiang/MoSLoRA.

Related papers

• Tensorized Clustered LoRA Merging for Multi-Task Interference [29.488565631016332]
  In multi-task settings, merging LoRA adapters trained on heterogeneous sources frequently causes textittask interference, degrading downstream performance.<n>We propose a tensorized clustered LoRA library targeting to address the task interference.<n> TC-LoRA achieves +1.4% accuracy on Phi-3 and +2.3% on Mistral-7B, demonstrating the effectiveness of TC-LoRA in LLM adaptation.
  arXiv  Detail & Related papers  (2025-08-06T01:26:43Z)
• Two Is Better Than One: Rotations Scale LoRAs [26.617019830475172]
  Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks.<n>Traditional gating mechanisms that route inputs to the best experts may fundamentally hinder LLMs' scalability.<n>We propose RadarGate, a novel geometrically inspired gating method that introduces rotational operations of LoRAs representations.
  arXiv  Detail & Related papers  (2025-05-29T07:22:43Z)
• 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) Current methods optimize LoRA by initializing with static singular value decomposition subsets, leading to suboptimal leveraging of pre-trained knowledge. We propose underlineGreat LunderlineoRunderlineA Mixture-of-Experunderlinet (GOAT) 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)
• A Stronger Mixture of Low-Rank Experts for Fine-Tuning Foundation Models [22.457766373989365]
  Low-Rank Adapters (LoRAs) have been substantially adopted across various fields, including instruction tuning and domain adaptation. To address the limited expressive capacity of LoRA, the Mixture-of-Expert (MoE) has been introduced for incorporating multiple LoRA adapters. We propose a new training strategy for MoE-LoRA, to stabilize and boost its feature learning procedure by multi-space projections.
  arXiv  Detail & Related papers  (2025-02-20T05:58:53Z)
• BeamLoRA: Beam-Constraint Low-Rank Adaptation [51.52097743781401]
  Low-Rank Adaptation (LoRA) has been widely adopted as one of the most effective parameter-efficient fine-tuning methods. We propose BeamLoRA, which conceptualizes each LoRA module as a beam where each rank naturally corresponds to a potential sub-solution.
  arXiv  Detail & Related papers  (2025-02-19T10:33:22Z)
• Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning [53.98941571078398]
  Low-Rank Adaptation (LoRA) is widely used for adapting large language models (LLMs) to specific domains due to its efficiency and modularity. Recent works adopt Mixture of Experts (MoE) by treating each LoRA module as an expert, thereby mitigating task interference through multiple specialized LoRA modules. While effective, these methods often isolate knowledge within individual tasks, failing to fully exploit the shared knowledge across related tasks. We propose Single-ranked Mixture of Experts LoRA (textbfSMoRA), which embeds MoE into LoRA by textittreating each rank as an
  arXiv  Detail & Related papers  (2025-01-25T06:56:39Z)
• MiLoRA: Efficient Mixture of Low-Rank Adaptation for Large Language Models Fine-tuning [9.91790333647256]
  Low-rank adaptation (LoRA) and its mixture-of-experts (MOE) variants are highly effective parameter-efficient fine-tuning (PEFT) methods. We propose Mixture of Low-Rank Adaptation (MiLoRA), a novel and efficient LoRA variant. MiLoRA differs from previous MOE-style LoRA methods by considering each LoRA module as an expert and employing a prompt-aware routing mechanism.
  arXiv  Detail & Related papers  (2024-10-23T17:04:40Z)
• Flat-LoRA: Low-Rank Adaptation over a Flat Loss Landscape [52.98187034726091]
  We introduce Flat-LoRA, which aims to identify a low-rank adaptation situated in a flat region of the full parameter space.<n>We show that Flat-LoRA improves both in-domain and out-of-domain generalization.
  arXiv  Detail & Related papers  (2024-09-22T11:24:10Z)
• FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations [39.88985198467528]
  We introduce a new approach called FLORA that enables federated fine-tuning on heterogeneous LoRA adapters. Our approach is noise-free and seamlessly supports heterogeneous LoRA adapters.
  arXiv  Detail & Related papers  (2024-09-09T18:21:23Z)
• LoRA-Pro: Are Low-Rank Adapters Properly Optimized? [121.0693322732454]
  Low-rank adaptation, also known as LoRA, has emerged as a prominent method for parameter-efficient fine-tuning of foundation models. Despite its computational efficiency, LoRA still yields inferior performance compared to full fine-tuning. We introduce LoRA-Pro, a method that enhances LoRA's performance by strategically adjusting the gradients of low-rank matrices.
  arXiv  Detail & Related papers  (2024-07-25T17:57:12Z)
• Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning [57.36978335727009]
  Low-Rank Adaptation (LoRA) offers an efficient way to fine-tune large language models (LLMs) In this paper, we propose a framework that adaptively retrieves and composes multiple LoRAs based on input prompts.
  arXiv  Detail & Related papers  (2024-06-24T05:24:41Z)
• Mixture of LoRA Experts [87.50120181861362]
  This paper introduces the Mixture of LoRA Experts (MoLE) approach, which harnesses hierarchical control and unfettered branch selection. The MoLE approach achieves superior LoRA fusion performance in comparison to direct arithmetic merging.
  arXiv  Detail & Related papers  (2024-04-21T11:59:53Z)
• Improving LoRA in Privacy-preserving Federated Learning [44.47315926976059]
  Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models. This paper proposes an efficient and effective version of LoRA, Federated Freeze A LoRA (FFA-LoRA), to alleviate these challenges.
  arXiv  Detail & Related papers  (2024-03-18T23:20:08Z)
• ResLoRA: Identity Residual Mapping in Low-Rank Adaption [96.59370314485074]
  We propose ResLoRA, an improved framework of low-rank adaptation (LoRA) Our method can achieve better results in fewer training steps without any extra trainable parameters or inference cost compared to LoRA. The experiments on NLG, NLU, and text-to-image tasks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2024-02-28T04:33:20Z)
• DoRA: Weight-Decomposed Low-Rank Adaptation [57.68678247436207]
  We introduce a novel weight decomposition analysis to investigate the inherent differences between FT and LoRA. Aiming to resemble the learning capacity of FT from the findings, we propose Weight-Decomposed Low-Rank Adaptation (DoRA) DoRA decomposes the pre-trained weight into two components, magnitude and direction, for fine-tuning.
  arXiv  Detail & Related papers  (2024-02-14T17:59:34Z)
• LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Models [104.23434818428062]
  We focus on the scenario where quantization and LoRA fine-tuning are applied together on a pre-trained model. We propose LoftQ (LoRA-Fine-Tuning-aware Quantization), a novel quantization framework. Experiments show that our method is highly effective and outperforms existing quantization methods.
  arXiv  Detail & Related papers  (2023-10-12T18:34:08Z)
• NOLA: Compressing LoRA using Linear Combination of Random Basis [22.76088132446952]
  We introduce NOLA, which overcomes the rank one lower bound present in LoRA. NOLA performs as well as LoRA models with much fewer number of parameters compared to LoRA with rank one, the best compression LoRA can archive.
  arXiv  Detail & Related papers  (2023-10-04T03:30:24Z)

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.