Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation

• URL: http://arxiv.org/abs/2509.19602v1
• Date: Tue, 23 Sep 2025 21:51:04 GMT
• Title: Parameter-Efficient Multi-Task Learning via Progressive Task-Specific Adaptation
• Authors: Neeraj Gangwar, Anshuka Rangi, Rishabh Deshmukh, Holakou Rahmanian, Yesh Dattatreya, Nickvash Kani,
• Abstract summary: We introduce task-specific multi-task adaptation, a novel parameter-efficient approach for multi-task learning.<n>Our approach achieves better relative improvement to single-task fine-tuning while reducing the number of trainable parameters.
• Score: 5.461305353111217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Parameter-efficient fine-tuning methods have emerged as a promising solution for adapting pre-trained models to various downstream tasks. While these methods perform well in single-task learning, extending them to multi-task learning exacerbates common challenges, such as task interference and negative transfer, due to the limited number of trainable parameters. To address these issues, we introduce progressive task-specific multi-task adaptation, a novel parameter-efficient approach for multi-task learning. This approach introduces adapter modules in a pre-trained model such that these modules are shared across all tasks in the initial layers and become progressively more task-specific in the later layers. The motivation is to reduce the conflicts among tasks by allowing transfer learning across all tasks in the initial layers and enabling task-specific learning toward the prediction heads. Additionally, we propose a gradient-based approach for computing task similarity and use this measure to allocate similar tasks to the shared adapter modules. Our task similarity method introduces minimal overhead in the pipeline. We evaluate our approach by adapting the Swin Transformer for dense prediction tasks. Experiments on the PASCAL and NYUD-v2 datasets demonstrate that our approach outperforms a fully fine-tuned multi-task model while requiring only one-fifth of the trainable parameters. This approach achieves better relative improvement to single-task fine-tuning while reducing the number of trainable parameters and surpasses the current state-of-the-art methods for parameter-efficient multi-task learning.

Related papers

• Pilot: Building the Federated Multimodal Instruction Tuning Framework [79.56362403673354]
  Our framework integrates two stages of "adapter on adapter" into the connector of the vision encoder and the LLM.<n>In stage 1, we extract task-specific features and client-specific features from visual information.<n>In stage 2, we build the cross-task Mixture-of-Adapters(CT-MoA) module to perform cross-task interaction.
  arXiv  Detail & Related papers  (2025-01-23T07:49:24Z)
• Why Train Everything? Tint a Single Layer for Multi-task Model Merging [17.496018757317824]
  Model merging integrates independently fine-tuned models into a single multi-task model, offering a flexible alternative to joint training.<n>Many existing model merging methods introduce additional task-specific components, increasing complexity and requiring extra modifications.<n>We propose Model Tinting, a lightweight yet highly effective approach that improves model merging by updating just a single layer.
  arXiv  Detail & Related papers  (2024-12-26T07:42:06Z)
• Quantifying Task Priority for Multi-Task Optimization [44.601029688423914]
  The goal of multi-task learning is to learn diverse tasks within a single unified network. We present a new method named connection strength-based optimization for multi-task learning.
  arXiv  Detail & Related papers  (2024-06-05T06:52:29Z)
• VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding [6.816428690763012]
  A standard approach to leverage large-scale pre-trained models is to fine-tune all model parameters for downstream tasks. We propose VMT-Adapter, which shares knowledge from multiple tasks to enhance cross-task interaction. We also propose VMT-Adapter-Lite, which further reduces the trainable parameters by learning shared parameters between down- and up-projections.
  arXiv  Detail & Related papers  (2023-12-14T08:25:04Z)
• AdaTask: A Task-aware Adaptive Learning Rate Approach to Multi-task Learning [19.201899503691266]
  We measure the task dominance degree of a parameter by the total updates of each task on this parameter. We propose a Task-wise Adaptive learning rate approach, AdaTask, to separate the emphaccumulative gradients and hence the learning rate of each task. Experiments on computer vision and recommender system MTL datasets demonstrate that AdaTask significantly improves the performance of dominated tasks.
  arXiv  Detail & Related papers  (2022-11-28T04:24:38Z)
• Pareto Manifold Learning: Tackling multiple tasks via ensembles of single-task models [50.33956216274694]
  In Multi-Task Learning (MTL), tasks may compete and limit the performance achieved on each other, rather than guiding the optimization to a solution. We propose textitPareto Manifold Learning, an ensembling method in weight space.
  arXiv  Detail & Related papers  (2022-10-18T11:20:54Z)
• Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks [36.34331439747556]
  We propose Polyhistor and Polyhistor-Lite to share information across different tasks with a few trainable parameters. Specifically, Polyhistor achieves competitive accuracy compared to the state-of-the-art while only using 10% of their trainable parameters.
  arXiv  Detail & Related papers  (2022-10-07T00:25:02Z)
• Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving [103.745551954983]
  In this paper, we investigate the transfer performance of various types of self-supervised methods, including MoCo and SimCLR, on three downstream tasks. We find that their performances are sub-optimal or even lag far behind the single-task baseline. We propose a simple yet effective pretrain-adapt-finetune paradigm for general multi-task training.
  arXiv  Detail & Related papers  (2022-09-19T12:15:31Z)
• Task Adaptive Parameter Sharing for Multi-Task Learning [114.80350786535952]
  Adaptive Task Adapting Sharing (TAPS) is a method for tuning a base model to a new task by adaptively modifying a small, task-specific subset of layers. Compared to other methods, TAPS retains high accuracy on downstream tasks while introducing few task-specific parameters. We evaluate our method on a suite of fine-tuning tasks and architectures (ResNet, DenseNet, ViT) and show that it achieves state-of-the-art performance while being simple to implement.
  arXiv  Detail & Related papers  (2022-03-30T23:16:07Z)
• Multi-task Supervised Learning via Cross-learning [102.64082402388192]
  We consider a problem known as multi-task learning, consisting of fitting a set of regression functions intended for solving different tasks. In our novel formulation, we couple the parameters of these functions, so that they learn in their task specific domains while staying close to each other. This facilitates cross-fertilization in which data collected across different domains help improving the learning performance at each other task.
  arXiv  Detail & Related papers  (2020-10-24T21:35:57Z)
• Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference [75.95287293847697]
  Two common challenges in developing multi-task models are often overlooked in literature. First, enabling the model to be inherently incremental, continuously incorporating information from new tasks without forgetting the previously learned ones (incremental learning) Second, eliminating adverse interactions amongst tasks, which has been shown to significantly degrade the single-task performance in a multi-task setup (task interference)
  arXiv  Detail & Related papers  (2020-07-24T14:44:46Z)

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.