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Model Merging in the Essential Subspace [78.5390284258307]
Model merging aims to integrate multiple task-specific fine-tuned models into a single multi-task model without additional training.<n>Despite extensive research, task interference remains a major obstacle that often undermines the performance of merged models.<n>We propose ESM (Essential Subspace Merging), a robust framework for effective model merging.
arXiv Detail & Related papers (2026-02-23T00:33:38Z)
Towards Minimizing Feature Drift in Model Merging: Layer-wise Task Vector Fusion for Adaptive Knowledge Integration [14.503741632243646]
Multi-task model merging aims to consolidate knowledge from multiple task-specific experts into a unified model.<n>Existing methods approach this by minimizing differences between task-specific experts and the unified model.<n>We propose Layer-wise Optimal Task Vector Merging, a technique that explicitly minimizes feature drift between task-specific experts and the unified model.
arXiv Detail & Related papers (2025-05-29T08:11:31Z)
Navigating the Accuracy-Size Trade-Off with Flexible Model Merging [16.936134010292232]
We propose FlexMerge, a novel data-free model merging framework.<n>We show that even modestly larger merged models can provide substantial accuracy improvements over a single model.<n>By offering fine-grained control over fused model size, FlexMerge provides a flexible, data-free, and high-performance solution.
arXiv Detail & Related papers (2025-05-29T07:50:32Z)
Unifying Multimodal Large Language Model Capabilities and Modalities via Model Merging [103.98582374569789]
Model merging aims to combine multiple expert models into a single model, thereby reducing storage and serving costs.<n>Previous studies have primarily focused on merging visual classification models or Large Language Models (LLMs) for code and math tasks.<n>We introduce the model merging benchmark for MLLMs, which includes multiple tasks such as VQA, Geometry, Chart, OCR, and Grounding, providing both LoRA and full fine-tuning models.
arXiv Detail & Related papers (2025-05-26T12:23:14Z)
AdaRank: Adaptive Rank Pruning for Enhanced Model Merging [23.649762835129167]
Model merging has emerged as a promising approach for unifying independently fine-tuned models into an integrated framework.<n>We propose AdaRank, a novel model merging framework that adaptively selects the most beneficial singular directions of task vectors to merge multiple models.<n>AdaRank consistently achieves state-of-the-art performance with various backbones and number of tasks, reducing the performance gap between fine-tuned models to nearly 1%.
arXiv Detail & Related papers (2025-03-28T06:49:06Z)
Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond [52.486290612938895]
We propose a novel method that leverages the semantic knowledge from the Segment Anything Model (SAM) to Grow the quality of fusion results and Enable downstream task adaptability.<n> Specifically, we design a Semantic Persistent Attention (SPA) Module that efficiently maintains source information via the persistent repository while extracting high-level semantic priors from SAM.<n>Our method achieves a balance between high-quality visual results and downstream task adaptability while maintaining practical deployment efficiency.
arXiv Detail & Related papers (2025-03-03T06:16:31Z)
RobustMerge: Parameter-Efficient Model Merging for MLLMs with Direction Robustness [28.437105789298244]
RobustMerge is a training-free parameter-efficient merging method with complementary parameter adaptation to maintain direction robustness.<n>We establish a benchmark consisting of diverse multimodal tasks, on which we conduct experiments to certify the outstanding performance and generalizability of our method.
arXiv Detail & Related papers (2025-02-24T13:52:05Z)
No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces [17.69597528370121]
Model merging integrates the weights of multiple task-specific models into a single multi-task model. Despite recent interest in the problem, a significant performance gap between the combined and single-task models remains. We show that alignment between singular components of task-specific and merged matrices strongly correlates with performance improvement.
arXiv Detail & Related papers (2025-02-07T14:22:56Z)
Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent [74.02034188307857]
Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data. We find existing methods inevitably discard task-specific information that, while causing conflicts, is crucial for performance. Our approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
arXiv Detail & Related papers (2025-01-02T12:45:21Z)
SuperMerge: An Approach For Gradient-Based Model Merging [9.136320029568305]
Large language models, such as ChatGPT, Claude, or LLaMA, are gigantic, monolithic, and possess the superpower to simultaneously support thousands of tasks. One challenge of using task-specific models is the incremental need for solving newer tasks after the model is already deployed for existing tasks. We propose a model merging based approach called SUPERMERGE. We experimentally demonstrate that SUPERMERGE outperforms existing model merging methods on common natural language processing and computer vision tasks.
arXiv Detail & Related papers (2024-12-09T20:03:14Z)
Optimizing Dense Visual Predictions Through Multi-Task Coherence and Prioritization [7.776434991976473]
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arXiv Detail & Related papers (2024-12-04T10:05:47Z)
Multi-Task Model Merging via Adaptive Weight Disentanglement [69.7292615212444]
We introduce an Adaptive Weight Disentanglement method for model merging.<n>We successfully extract redundant vectors, and after their subtraction, the task vectors retain robust performance.
arXiv Detail & Related papers (2024-11-27T20:08:55Z)
Task Weighting through Gradient Projection for Multitask Learning [5.5967570276373655]
In multitask learning, conflicts between task gradients are a frequent issue degrading a model's training performance. In this work, we present a method to adapt the Gradient Projection algorithm PCGrad to simultaneously perform task prioritization. Our approach differs from traditional task weighting performed by scaling task losses in that our weighting scheme applies only in cases where tasks are in conflict, but lets the training proceed unhindered otherwise.
arXiv Detail & Related papers (2024-09-03T11:17:44Z)
EMR-Merging: Tuning-Free High-Performance Model Merging [55.03509900949149]
We show that Elect, Mask & Rescale-Merging (EMR-Merging) shows outstanding performance compared to existing merging methods. EMR-Merging is tuning-free, thus requiring no data availability or any additional training while showing impressive performance.
arXiv Detail & Related papers (2024-05-23T05:25:45Z)
AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
arXiv Detail & Related papers (2023-10-04T04:26:33Z)
An Efficient General-Purpose Modular Vision Model via Multi-Task Heterogeneous Training [79.78201886156513]
We present a model that can perform multiple vision tasks and can be adapted to other downstream tasks efficiently. Our approach achieves comparable results to single-task state-of-the-art models and demonstrates strong generalization on downstream tasks.
arXiv Detail & Related papers (2023-06-29T17:59:57Z)
ZipIt! Merging Models from Different Tasks without Training [20.2479633507354]
"ZipIt!" is a general method for merging two arbitrary models of the same architecture. We find that these two changes combined account for 20-60% improvement over prior work.
arXiv Detail & Related papers (2023-05-04T17:59:58Z)
Exposing and Addressing Cross-Task Inconsistency in Unified Vision-Language Models [80.23791222509644]
Inconsistent AI models are considered brittle and untrustworthy by human users. We find that state-of-the-art vision-language models suffer from a surprisingly high degree of inconsistent behavior across tasks. We propose a rank correlation-based auxiliary training objective, computed over large automatically created cross-task contrast sets.
arXiv Detail & Related papers (2023-03-28T16:57:12Z)
eP-ALM: Efficient Perceptual Augmentation of Language Models [70.47962271121389]
We propose to direct effort to efficient adaptations of existing models, and propose to augment Language Models with perception. Existing approaches for adapting pretrained models for vision-language tasks still rely on several key components that hinder their efficiency. We show that by freezing more than 99% of total parameters, training only one linear projection layer, and prepending only one trainable token, our approach (dubbed eP-ALM) significantly outperforms other baselines on VQA and Captioning.
arXiv Detail & Related papers (2023-03-20T19:20:34Z)
Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
arXiv Detail & Related papers (2022-12-19T20:46:43Z)
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 Learning as a Bargaining Game [63.49888996291245]
In Multi-task learning (MTL), a joint model is trained to simultaneously make predictions for several tasks. Since the gradients of these different tasks may conflict, training a joint model for MTL often yields lower performance than its corresponding single-task counterparts. We propose viewing the gradients combination step as a bargaining game, where tasks negotiate to reach an agreement on a joint direction of parameter update.
arXiv Detail & Related papers (2022-02-02T13:21:53Z)
Uni-Perceiver: Pre-training Unified Architecture for Generic Perception for Zero-shot and Few-shot Tasks [73.63892022944198]
We present a generic perception architecture named Uni-Perceiver. It processes a variety of modalities and tasks with unified modeling and shared parameters. Results show that our pre-trained model without any tuning can achieve reasonable performance even on novel tasks.
arXiv Detail & Related papers (2021-12-02T18:59:50Z)
Conflict-Averse Gradient Descent for Multi-task Learning [56.379937772617]
A major challenge in optimizing a multi-task model is the conflicting gradients. We introduce Conflict-Averse Gradient descent (CAGrad) which minimizes the average loss function. CAGrad balances the objectives automatically and still provably converges to a minimum over the average loss.
arXiv Detail & Related papers (2021-10-26T22:03:51Z)
Multi-Task Learning with Sequence-Conditioned Transporter Networks [67.57293592529517]
We aim to solve multi-task learning through the lens of sequence-conditioning and weighted sampling. We propose a new suite of benchmark aimed at compositional tasks, MultiRavens, which allows defining custom task combinations. Second, we propose a vision-based end-to-end system architecture, Sequence-Conditioned Transporter Networks, which augments Goal-Conditioned Transporter Networks with sequence-conditioning and weighted sampling.
arXiv Detail & Related papers (2021-09-15T21:19:11Z)
Rethinking Hard-Parameter Sharing in Multi-Task Learning [20.792654758645302]
Hard parameter sharing in multi-task learning (MTL) allows tasks to share some of model parameters, reducing storage cost and improving prediction accuracy. The common sharing practice is to share bottom layers of a deep neural network among tasks while using separate top layers for each task. Using separate bottom-layer parameters could achieve significantly better performance than the common practice.
arXiv Detail & Related papers (2021-07-23T17:26:40Z)

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