FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs

• URL: http://arxiv.org/abs/2511.00050v1
• Date: Tue, 28 Oct 2025 12:45:45 GMT
• Title: FLoRA: Fused forward-backward adapters for parameter efficient fine-tuning and reducing inference-time latencies of LLMs
• Authors: Dhananjaya Gowda, Seoha Song, Junhyun Lee, Harshith Goka,
• Abstract summary: We propose a family of fused forward-backward adapters (FFBA) for parameter-efficient fine-tuning of large language models (LLMs) on downstream tasks.<n> Experimental results show that the proposed FFB adapters perform significantly better than the popularly used LoRA in both accuracy and latency.
• Score: 7.771813594229729
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As the large language models (LLMs) grow in size each day, efficient training and fine-tuning has never been as important as nowadays. This resulted in the great interest in parameter efficient fine-tuning (PEFT), and effective methods including low-rank adapters (LoRA) has emerged. Although the various PEFT methods have been studied extensively in the recent years, the greater part of the subject remains unexplored with the huge degree of freedom. In this paper, we propose FLoRA, a family of fused forward-backward adapters (FFBA) for parameter-efficient fine-tuning of LLMs on downstream tasks. The FFBA combine ideas from the popular LoRA and parallel adapters to improve the overall fine-tuning accuracies. At the same time, latencies are minimized by fusing the forward and backward adapters into existing projection layers of the base model. Experimental results show that the proposed FFB adapters perform significantly better than the popularly used LoRA in both accuracy and latency for a similar parameter budget.

Related papers

• Faster Than SVD, Smarter Than SGD: The OPLoRA Alternating Update [50.36542772932594]
  Low-Rank Adaptation (LoRA) fine-tunes large models by learning low-rank updates on top of frozen weights.<n>There is still a gap between full training with low-rank projections (SVDLoRA) and LoRA fine-tuning, indicating that LoRA steps can be further improved.
  arXiv  Detail & Related papers  (2025-09-24T10:32:50Z)
• DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation [44.99833362998488]
  Decoupled Low-rank Adaptation (DeLoRA) is a novel finetuning method that normalizes and scales learnable low-rank matrices.<n>We show that DeLoRA matches or surpasses performance of competing PEFT methods, while exhibiting stronger robustness.
  arXiv  Detail & Related papers  (2025-03-23T22:00:56Z)
• EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition [2.5269004336032186]
  Efficient Weight-Decomposed Low-Rank Adaptation (EDoRA) is a novel PEFT method that decomposes pre-trained weights into magnitude and directional components.<n>EDoRA achieves competitive or superior performance compared to state-of-the-art methods, such as LoRA and DoRA.
  arXiv  Detail & Related papers  (2025-01-21T11:42:09Z)
• Replay-Free Continual Low-Rank Adaptation with Dynamic Memory [62.85596937435928]
  We revisit continual learning, which enables pre-trained vision transformers (ViTs) to sequentially fine-tune on new downstream tasks over time.<n>Recent studies highlight a crossover between CL techniques and parameter-efficient fine-tuning.<n>We propose a novel PEFT-CL method called Dual Low-Rank Adaptation (DualLoRA)
  arXiv  Detail & Related papers  (2024-11-01T14:28:39Z)
• 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)
• 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$^2$ : Multi-Scale Low-Rank Approximations for Fine-Tuning Large Language Models [3.7049613588433497]
  Low-Rank Adaptation (LoRA) significantly reduces the number of trainable parameters for fine-tuning. We extend the LoRA to multiple scales, dubbed as LoRA$2$.
  arXiv  Detail & Related papers  (2024-08-13T12:31:30Z)
• 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)
• MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning [71.50432879573614]
  Low-rank adaptation (LoRA) is based on the idea that the adaptation process is intrinsically low-dimensional.<n>We present MELoRA, a mini-ensemble low-rank adapters that uses fewer trainable parameters while maintaining a higher rank.<n>Our experimental results show that, compared to LoRA, MELoRA achieves better performance with 8 times fewer trainable parameters on natural language understanding tasks and 36 times fewer trainable parameters on instruction following tasks.
  arXiv  Detail & Related papers  (2024-02-27T07:14:12Z)
• 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)

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.