# No Task Left Behind: Isotropic Model Merging with Common and Task-Specific Subspaces

arXiv: https://arxiv.org/abs/2502.04959 · HTML: https://mlsanity.com/paper/2502.04959/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 59 references that are in the map.
- First posted on arXiv 7 Feb 2025.

## Builds on (most cited first, 30 of 40)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [The Power of Scale for Parameter-Efficient Prompt Tuning](https://mlsanity.com/paper/2104.08691/) (2021)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://mlsanity.com/paper/1709.00029/) (2017)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time](https://mlsanity.com/paper/2203.05482/) (2022)
- [Editing Models with Task Arithmetic](https://mlsanity.com/paper/2212.04089/) (2022)
- [TIES-Merging: Resolving Interference When Merging Models](https://mlsanity.com/paper/2306.01708/) (2023)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch](https://mlsanity.com/paper/2311.03099/) (2023)
- [Merging Models with Fisher-Weighted Averaging](https://mlsanity.com/paper/2111.09832/) (2021)
- [Rotation Equivariant CNNs for Digital Pathology](https://mlsanity.com/paper/1806.03962/) (2018)
- [Deep Learning for Classical Japanese Literature](https://mlsanity.com/paper/1812.01718/) (2018)
- [Dataless Knowledge Fusion by Merging Weights of Language Models](https://mlsanity.com/paper/2212.09849/) (2022)
- [Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation](https://mlsanity.com/paper/1404.0736/) (2014)
- [Challenges in Representation Learning: A report on three machine learning contests](https://mlsanity.com/paper/1307.0414/) (2013)
- [Task Arithmetic in the Tangent Space: Improved Editing of Pre-Trained Models](https://mlsanity.com/paper/2305.12827/) (2023)
- [Patching open-vocabulary models by interpolating weights](https://mlsanity.com/paper/2208.05592/) (2022)
- [Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications](https://mlsanity.com/paper/1511.06530/) (2015)
- [Task Singular Vectors: Reducing Task Interference in Model Merging](https://mlsanity.com/paper/2412.00081/) (2024)
- [Model Breadcrumbs: Scaling Multi-Task Model Merging with Sparse Masks](https://mlsanity.com/paper/2312.06795/) (2023)
- [Localizing Task Information for Improved Model Merging and Compression](https://mlsanity.com/paper/2405.07813/) (2024)
- [Model merging with SVD to tie the Knots](https://mlsanity.com/paper/2410.19735/) (2024)

## Built on by (most cited first, 30 of 96)

- [Accurate and Efficient Low-Rank Model Merging in Core Space](https://mlsanity.com/paper/2509.17786/) (2025)
- [MergeVLA: Cross-Skill Model Merging Toward a Generalist Vision-Language-Action Agent](https://mlsanity.com/paper/2511.18810/) (2025)
- [RegMean++: Enhancing Effectiveness and Generalization of Regression Mean for Model Merging](https://mlsanity.com/paper/2508.03121/) (2025)
- [Scalable Model Merging with Progressive Layer-wise Distillation](https://mlsanity.com/paper/2502.12706/) (2025)
- [AdaRank: Adaptive Rank Pruning for Enhanced Model Merging](https://mlsanity.com/paper/2503.22178/) (2025)
- [Subspace-Boosted Model Merging](https://mlsanity.com/paper/2506.16506/) (2025)
- [Expert Merging: Model Merging with Unsupervised Expert Alignment and Importance-Guided Layer Chunking](https://mlsanity.com/paper/2509.25712/) (2025)
- [DC-Merge: Improving Model Merging with Directional Consistency](https://mlsanity.com/paper/2603.06242/) (2026)
- [Demystifying Mergeability: Interpretable Properties to Predict Model Merging Success](https://mlsanity.com/paper/2601.22285/) (2026)
- [Model Merging in the Essential Subspace](https://mlsanity.com/paper/2602.20208/) (2026)
- [Why Do More Experts Fail? A Theoretical Analysis of Model Merging](https://mlsanity.com/paper/2505.21226/) (2025)
- [Model Unmerging: Making Your Models Unmergeable for Secure Model Sharing](https://mlsanity.com/paper/2509.01548/) (2025)
- [Transporting Task Vectors across Different Architectures without Training](https://mlsanity.com/paper/2602.12952/) (2026)
- [ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation](https://mlsanity.com/paper/2603.02945/) (2026)
- [MASS: MoErging through Adaptive Subspace Selection](https://mlsanity.com/paper/2504.05342/) (2025)
- [Rethinking Layer-wise Model Merging through Chain of Merges](https://mlsanity.com/paper/2508.21421/) (2025)
- [A Systematic Study of In-the-Wild Model Merging for Large Language Models](https://mlsanity.com/paper/2511.21437/) (2025)
- [Spectral Imbalance Causes Forgetting in Low-Rank Continual Adaptation](https://mlsanity.com/paper/2602.00722/) (2026)
- [When Shared Knowledge Hurts: Spectral Over-Accumulation in Model Merging](https://mlsanity.com/paper/2602.05536/) (2026)
- [From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging](https://mlsanity.com/paper/2506.14126/) (2025)
- [Gradient-Sign Masking for Task Vector Transport Across Pre-Trained Models](https://mlsanity.com/paper/2510.09658/) (2025)
- [Orthogonal Model Merging](https://mlsanity.com/paper/2602.05943/) (2026)
- [Label-Free Cross-Task LoRA Merging with Null-Space Compression](https://mlsanity.com/paper/2603.26317/) (2026)
- [Decoupled DiLoCo for Resilient Distributed Pre-training](https://mlsanity.com/paper/2604.21428/) (2026)
- [Towards Efficient Multimodal Unified Reasoning Model via Model Merging](https://mlsanity.com/paper/2510.08987/) (2025)
- [Dataless Weight Disentanglement in Task Arithmetic via Kronecker-Factored Approximate Curvature](https://mlsanity.com/paper/2602.17385/) (2026)
- [Compress then Merge: From Multiple LoRAs into One Low-Rank Adapter](https://mlsanity.com/paper/2606.03723/) (2026)
- [Backdoor Vectors: a Task Arithmetic View on Backdoor Attacks and Defenses](https://mlsanity.com/paper/2510.08016/) (2025)
- [Model Merging Improves Zero-Shot Generalization in Bioacoustic Foundation Models](https://mlsanity.com/paper/2511.05171/) (2025)
- [Stay Unique, Stay Efficient: Preserving Model Personality in Multi-Task Merging](https://mlsanity.com/paper/2512.01461/) (2025)

## Abstract (arXiv)

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. In this paper, we investigate the key characteristics of task matrices -- weight update matrices applied to a pre-trained model -- that enable effective merging. We show that alignment between singular components of task-specific and merged matrices strongly correlates with performance improvement over the pre-trained model. Based on this, we propose an isotropic merging framework that flattens the singular value spectrum of task matrices, enhances alignment, and reduces the performance gap. Additionally, we incorporate both common and task-specific subspaces to further improve alignment and performance. Our proposed approach achieves state-of-the-art performance on vision and language tasks across various sets of tasks and model scales. This work advances the understanding of model merging dynamics, offering an effective methodology to merge models without requiring additional training. Code is available at https://github.com/danielm1405/iso-merging .