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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.
| Date | Stars |
|---|---|
| 2026-07-31 | 771 |
| 2026-08-06 | 772 |
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growth rate 0.00%/day
A comprehensive list of papers about **'[Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. ACM Computing Surveys, 2026.](https://arxiv.org/pdf/2408.07666)'**.
---
> [!IMPORTANT]
> Contributions welcome:
>
> [Contact us](#contact) or submit a pull request for unlisted relevant papers, content clarifications, or categorization adjustments, and update relevant information once your paper is accepted. Thank you!
---
## 💥 News 💥
- 🔥🔥🔥 Our [survey](https://dl.acm.org/doi/10.1145/3787849), accepted by ACM Computing Surveys, please [cite](#citation) it or the library if helpful.
- 🔥🔥🔥 We flagged papers using models of size **$\geq$ 7B** (or small-sized mainstream LLMs) in their experiments.
---
## Abstract
>
> Model merging is an efficient empowerment technique in the machine learning community that does not require the collection of raw training data and does not require expensive computation. As model merging becomes increasingly prevalent across various fields, it is crucial to understand the available model merging techniques comprehensively. However, there is a significant gap in the literature regarding a systematic and thorough review of these techniques. To address this gap, this survey provides a comprehensive overview of model merging methods and theories, their applications in various domains and settings, and future research directions. Specifically, we first propose a new taxonomic approach that exhaustively discusses existing model merging methods. Secondly, we discuss the application of model merging techniques in large language models, multimodal large language models, and 10+ machine learning subfields, including continual learning, multi-task learning, few-shot learning, etc. Finally, we highlight the remaining challenges of model merging and discuss future research directions.
<center>
<img src="./imgs/intro.png" alt="Model Merging" width="800"/>
</center>
## Citation
If you find our paper or this resource helpful, please consider cite:
```
@article{yang2026ModelMergingSurvey,
author = {Yang, Enneng and Shen, Li and Guo, Guibing and Wang, Xingwei and Cao, Xiaochun and Zhang, Jie and Tao, Dacheng},
title = {Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications, and Opportunities},
year = {2026},
issue_date = {June 2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {58},
number = {8},
issn = {0360-0300},
url = {https://doi.org/10.1145/3787849},
doi = {10.1145/3787849},
journal = {ACM Computing Surveys},
month = feb,
articleno = {216},
numpages = {41}
}
```
Thanks!
******
## Framework
- [💥 News 💥](#-news-)
- [Abstract](#abstract)
- [Citation](#citation)
- [Framework](#framework)
- [Survey](#survey)
- [Benchmark/Evaluation](#benchmarkevaluation)
- [Advanced Methods](#advanced-methods)
- [Pre-Merging Methods](#pre-merging-methods)
- [Better Fine-tuning](#better-fine-tuning)
- [Linearization Fine-tuning](#linearization-fine-tuning)
- [Subspace Fine-tuning](#subspace-fine-tuning)
- [Sharpness-aware Fine-tuning](#sharpness-aware-fine-tuning)
- [Others](#others)
- [Architecture Transformation](#architecture-transformation)
- [Weight Alignment](#weight-alignment)
- [During Merging Methods](#during-merging-methods)
- [Basic Merging Methods](#basic-merging-methods)
- [Weighted-based Merging Methods](#weighted-based-merging-methods)
- [Subspace-based Merging Method (Sparse or Low-rank Subspace)](#subspace-based-merging-method-sparse-or-low-rank-subspace)
- [Routing-based Merging Methods (Dynamic Merging)](#routing-based-merging-methods-dynamic-merging)
- [Post-calibration based Methods](#post-calibration-based-methods)
- [Other Merging Methods](#other-merging-methods)
- [Theories or Analysis of Model Merging](#theories-or-analysis-of-model-merging)
- [Application of Model Merging in Foundation Models](Excerpt of 155,349 characters
Read on GitHub
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:2b5b621ad311f74d, topic:large-language-models, topic:foundation-models
matched fp:2b5b621ad311f74d, topic:diffusion-models