arXiv Computer Vision

CRLoMTL: Conflict-Resolution through Low-rank matrices for Multi-Task Learning

CRLoMTL is a parameter‑efficient multi‑task learning framework that mitigates task interference by operating in a low‑rank parameter‑update space rather than gradient space. It learns task‑specific LoRA updates and fuses them through a shared‑subspace resolver based on singular value decomposition, producing a single deployment‑time update that can be folded into the backbone. Experiments on Swin Transformer backbones across PASCAL‑Context, NYUD‑v2, and CelebA show that CRLoMTL improves accuracy over single‑task baselines while training only a small fraction of the parameters.

arXiv Machine Learning

Oct 15

Task-Driven Subspace Decomposition for Knowledge Sharing and Isolation in LoRA-based Continual Learning

The paper introduces LoDA, a LoRA-based continual learning method that decomposes update spaces into task‑shared and truly task‑specific subspaces using energy‑based objectives. By fixing down‑projections and learning up‑projections with Gradient‑Aligned Optimization, LoDA recalibrates general updates in closed form after each task, aiming to improve knowledge sharing and isolation. Experiments show that LoDA outperforms existing CL methods.

By Lingfeng He, De Cheng, Huaijie Wang, Xi Yang, Nannan Wang, Xinbo Gao

fine-tuning

arXiv Machine Learning

Jun 16

Concrete Subspace Learning based Interference Elimination for Multi-task Model Fusion

arXiv:2312. 06173v2 Announce Type: replace Abstract: Merging models fine-tuned from a common, extensively pre-trained large model but specialized for different tasks has been demonstrated as a cheap and scalable strategy to construct a multi-task model that performs well across diverse tasks.

By Anke Tang, Xianglin Luo, Li Shen, Yong Luo, Liang Ding, Han Hu, Bo Du, Dacheng Tao

fine-tuning

arXiv AI

Jun 18

Essential Subspace Merging for Multi-Task Learning

arXiv:2606. 19164v1 Announce Type: cross Abstract: Model merging aims to enable multi-task learning by integrating the capabilities of multiple models fine-tuned from the same pre-trained checkpoint into a single model.

By Longhua Li, Lei Qi, Xin Geng, Qi Tian

fine-tuning

arXiv AI

Oct 15

Data-Aware Model Merging in the Essential Subspace for Multi-Task Knowledge Aggregation

arXiv:2606.19164v4 Announce Type: replace-cross Abstract: Model merging enables training-free multi-task learning by aggregating knowledge from models fine-tuned from a shared checkpoint, but their t...

By Longhua Li, Lei Qi, Qi Tian, Xin Geng

fine-tuning

arXiv Machine Learning

Jun 18

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

arXiv:2606. 18627v1 Announce Type: new Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a single multi-task model.

By Ningyuan Shi, Zhipeng Zhou, Hao Wang, Chunyan Miao, Peilin Zhao

fine-tuning benchmarks

arXiv Machine Learning

Aug 10

Training-free Task Classification for Multi-Task Model Merging

arXiv:2606. 22589v2 Announce Type: replace Abstract: Ever since the advent of foundation models and the pre-training-finetuning paradigm, there have been numerous efforts to merge multiple task-specific experts into a single multi-task model.

By Jungyong Son, Jinwook Jung, Sungyong Baik

computer-vision nlp fine-tuning benchmarks

arXiv AI

Oct 21

Mitigating Model Merging Conflicts via Knowledge-Aware Task Vector Purification

arXiv:2510.14697v2 Announce Type: replace Abstract: Model merging aims to integrate task-specific abilities from individually fine-tuned models into a single model without extra training. In recent m...

By Bang An, Yibo Yang, Philip Torr, Bernard Ghanem

fine-tuning efficiency

arXiv Machine Learning

Jul 24

CT-Merging: Consensus Directions and Task-Level Scaling for LoRA Adapter Merging

arXiv:2607. 20561v1 Announce Type: new Abstract: LoRA adapters provide an efficient way to specialize a pretrained model for many downstream tasks, but deploying one adapter per task requires adapter storage and task selection at inference time.

By Keumseo Ryum, Joonhyuk Kang

fine-tuning benchmarks

Hugging Face Trending Papers

Jun 25

Learning to Recover Task Experts from a Multi-Task Merged Model

Multi-task model merging aims to consolidate several task-specific experts into a unified model, yet static merging consistently suffers from parameter interference. While dynamic merging models aim to bridge this gap, many works rely on the costly storage and loading of redundant expert components at inference.

nlp

arXiv Computation and Language

Oct 22

GRAM: Group-wise Rank-Aware Model Merging via Subspace Alignment

arXiv:2610.29110v1 Announce Type: cross Abstract: Merging different models into a single model is highly desirable for multi-task deployment, particularly in regulated domains like healthcare. In suc...

By Di Wu, Shuaidong Pan

fine-tuning safety

arXiv Computer Vision

Aug 21

CKAA: Cross-subspace Knowledge Alignment and Aggregation for Robust Continual Learning

arXiv:2507. 09471v4 Announce Type: replace Abstract: Continual Learning (CL) empowers AI models to continuously learn from sequential task streams.

By Lingfeng He, De Cheng, Zhiheng Ma, Huaijie Wang, Dingwen Zhang, Nannan Wang, Xinbo Gao

fine-tuning safety

arXiv AI

Oct 16

Learning Not To Interfere: Adapting Experts For Model Merging

The paper introduces Resolving Interference (RI), a lightweight adaptation framework that reduces cross‑task interference when merging expert models. RI disentangles expert models to be functionally orthogonal to other tasks, using only unlabeled auxiliary data, and improves merging performance by up to 3.8% and generalization to unseen domains by up to 2.3%. The method is robust to the source of auxiliary input and less sensitive to hyperparameter tuning.

By Pratik Ramesh, George Stoica, Arun Iyer, Leshem Choshen, Judy Hoffman

benchmarks

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