Towards Efficient Post-Training via Fourier-Driven Adapter Architectures

• URL: http://arxiv.org/abs/2512.22378v1
• Date: Fri, 26 Dec 2025 20:50:49 GMT
• Title: Towards Efficient Post-Training via Fourier-Driven Adapter Architectures
• Authors: Donggyun Bae, Jongil Park,
• Abstract summary: We propose a novel framework, termed Fourier-Activated Adapter (FAA), for parameter-efficient fine-tuning of large pre-trained language models.<n>FAA decomposes intermediate representations into complementary low- and high-frequency components, enabling frequency-aware modulation of semantic information.<n>FAA consistently achieves competitive or superior performance compared to existing parameter-efficient fine-tuning methods.
• Score: 1.18885605647513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel framework, termed Fourier-Activated Adapter (FAA), for parameter-efficient fine-tuning of large pre-trained language models. By incorporating random Fourier features into lightweight adapter modules, FAA decomposes intermediate representations into complementary low- and high-frequency components, enabling frequency-aware modulation of semantic information. This design allows the model to selectively emphasize informative frequency bands during adaptation while preserving the representational capacity of the frozen backbone. Extensive experiments on GLUE, E2E NLG, and instruction-tuning benchmarks demonstrate that FAA consistently achieves competitive or superior performance compared to existing parameter-efficient fine-tuning methods, while maintaining low computational and memory overhead. Ablation studies further verify the effectiveness of frequency-aware activation and adaptive weighting mechanisms, highlighting FAA as a robust and efficient approach for post-training large language models.

Related papers

• Benchmarking Few-shot Transferability of Pre-trained Models with Improved Evaluation Protocols [123.73663884421272]
  Few-shot transfer has been revolutionized by stronger pre-trained models and improved adaptation algorithms.<n>We establish FEWTRANS, a comprehensive benchmark containing 10 diverse datasets.<n>By releasing FEWTRANS, we aim to provide a rigorous "ruler" to streamline reproducible advances in few-shot transfer learning research.
  arXiv  Detail & Related papers  (2026-02-28T05:41:57Z)
• Communication-Efficient Wireless Federated Fine-Tuning for Large-Scale AI Models [13.742950928229078]
  Low-Rank Adaptation (LoRA) addresses these issues by training compact, low-rank matrices instead of fully fine-tuning large models.<n>This paper introduces a wireless federated LoRA fine-tuning framework that optimize both learning performance and communication efficiency.
  arXiv  Detail & Related papers  (2025-05-01T06:15:38Z)
• LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning [47.77830360814755]
  Location-aware Cosine Adaptation (LoCA) is a novel frequency-domain parameter-efficient fine-tuning method based on Discrete inverse Cosine Transform (iDCT)<n>Our analysis reveals that frequency-domain decomposition with carefully selected frequency components can surpass the expressivity of traditional low-rank-based methods.<n>Experiments on diverse language and vision fine-tuning tasks demonstrate that LoCA offers enhanced parameter efficiency while maintains computational feasibility comparable to low-rank-based methods.
  arXiv  Detail & Related papers  (2025-02-05T04:14:34Z)
• FreqMixFormerV2: Lightweight Frequency-aware Mixed Transformer for Human Skeleton Action Recognition [9.963966059349731]
  FreqMixForemrV2 is built upon the Frequency-aware Mixed Transformer (FreqMixFormer) for identifying subtle and discriminative actions.<n>The proposed model achieves a superior balance between efficiency and accuracy, outperforming state-of-the-art methods with only 60% of the parameters.
  arXiv  Detail & Related papers  (2024-12-29T23:52:40Z)
• Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves [123.07450481623124]
  We propose Skip Tuning as a novel paradigm for adapting vision-language models to downstream tasks.<n>Unlike existing PT or adapter-based methods, Skip Tuning applies Layer-wise Skipping (LSkip) and Class-wise Skipping (CSkip) upon the FT baseline without introducing extra context vectors or adapter modules.
  arXiv  Detail & Related papers  (2024-12-16T07:33:23Z)
• Visual Fourier Prompt Tuning [63.66866445034855]
  We propose the Visual Fourier Prompt Tuning (VFPT) method as a general and effective solution for adapting large-scale transformer-based models. Our approach incorporates the Fast Fourier Transform into prompt embeddings and harmoniously considers both spatial and frequency domain information. Our results demonstrate that our approach outperforms current state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2024-11-02T18:18:35Z)
• Efficient Language Model Architectures for Differentially Private Federated Learning [21.280600854272716]
  Cross-device federated learning (FL) is a technique that trains a model on data distributed across typically millions of edge devices without data leaving the devices. In centralized training of language models, adaptives are preferred as they offer improved stability and performance. We propose a scale-in Coupled Input Forget Gate (SI CIFG) recurrent network by modifying the sigmoid and tanh activations in neural recurrent cell.
  arXiv  Detail & Related papers  (2024-03-12T22:21:48Z)
• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization [71.87335804334616]
  Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. The training process of Large Language Models (LLMs) generally incurs the update of significant parameters. This paper proposes an efficient partial prompt tuning approach to improve performance and efficiency simultaneously.
  arXiv  Detail & Related papers  (2023-10-23T16:37:59Z)
• Approximated Prompt Tuning for Vision-Language Pre-trained Models [54.326232586461614]
  In vision-language pre-trained models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks. We propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning.
  arXiv  Detail & Related papers  (2023-06-27T05:43:47Z)
• Fast-FNet: Accelerating Transformer Encoder Models via Efficient Fourier Layers [0.0]
  Transformer-based language models utilize the attention mechanism for substantial performance improvements in almost all natural language processing (NLP) tasks. Recent works focused on eliminating the disadvantages of computational inefficiency and showed that transformer-based models can still reach competitive results without the attention layer. A pioneering study proposed the FNet, which replaces the attention layer with the Fourier Transform (FT) in the transformer encoder architecture.
  arXiv  Detail & Related papers  (2022-09-26T16:23:02Z)

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.