# Autoregressive Pretraining with Mamba in Vision

arXiv: https://arxiv.org/abs/2406.07537 · HTML: https://mlsanity.com/paper/2406.07537/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Vision Mamba backbones, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 54 of its 63 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Vision Mamba backbones](https://mlsanity.com/ssm/vision-mamba-backbones/)

## Builds on (most cited first, 30 of 54)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [Rethinking Attention with Performers](https://mlsanity.com/paper/2009.14794/) (2020)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Natural Adversarial Examples](https://mlsanity.com/paper/1907.07174/) (2019)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Designing Network Design Spaces](https://mlsanity.com/paper/2003.13678/) (2020)
- [RWKV: Reinventing RNNs for the Transformer Era](https://mlsanity.com/paper/2305.13048/) (2023)

## Built on by (most cited first, 22 of 22)

- [Beyond Next-Token: Next-X Prediction for Autoregressive Visual Generation](https://mlsanity.com/paper/2502.20388/) (2025)
- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset](https://mlsanity.com/paper/2410.00379/) (2024)
- [Scaling Laws in Patchification: An Image Is Worth 50,176 Tokens And More](https://mlsanity.com/paper/2502.03738/) (2025)
- [MAP: Unleashing Hybrid Mamba-Transformer Vision Backbone's Potential with Masked Autoregressive Pretraining](https://mlsanity.com/paper/2410.00871/) (2024)
- [RoMA: Scaling up Mamba-based Foundation Models for Remote Sensing](https://mlsanity.com/paper/2503.10392/) (2025)
- [Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency](https://mlsanity.com/paper/2410.07599/) (2024)
- [Vision Mamba in Remote Sensing: A Comprehensive Survey of Techniques, Applications and Outlook](https://mlsanity.com/paper/2505.00630/) (2025)
- [ViT-Linearizer: Distilling Quadratic Knowledge into Linear-Time Vision Models](https://mlsanity.com/paper/2504.00037/) (2025)
- [REOrdering Patches Improves Vision Models](https://mlsanity.com/paper/2505.23751/) (2025)
- [EMMA: Empowering Multi-modal Mamba with Structural and Hierarchical Alignment](https://mlsanity.com/paper/2410.05938/) (2024)
- [Surface Vision Mamba: Leveraging Bidirectional State Space Model for Efficient Spherical Manifold Representation](https://mlsanity.com/paper/2501.14679/) (2025)
- [BSMamba: Brightness and Semantic Modeling for Long-Range Interaction in Low-Light Image Enhancement](https://mlsanity.com/paper/2506.18346/) (2025)
- [RNN as Linear Transformer: A Closer Investigation into Representational Potentials of Visual Mamba Models](https://mlsanity.com/paper/2511.18380/) (2025)
- [Stochastic Layer-Wise Shuffle for Improving Vision Mamba Training](https://mlsanity.com/paper/2408.17081/) (2024)
- [Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing](https://mlsanity.com/paper/2502.00594/) (2025)
- [An overview of neural architectures for self-supervised audio representation learning from masked spectrograms](https://mlsanity.com/paper/2509.18691/) (2025)
- [How to Build Robust, Scalable Models for GSV-Based Indicators in Neighborhood Research](https://mlsanity.com/paper/2601.06443/) (2026)
- [Separators in Enhancing Autoregressive Pretraining for Vision Mamba](https://mlsanity.com/paper/2603.03806/) (2026)
- [Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation](https://mlsanity.com/paper/2605.14799/) (2026)
- [Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers](https://mlsanity.com/paper/2606.14757/) (2026)
- [Pre-training, Reasoning, Benchmarking: X-ray Report Generation on CheXpert Plus Dataset](https://mlsanity.com/paper/2610.08813/) (2026)

## Abstract (arXiv)

The vision community has started to build with the recently developed state space model, Mamba, as the new backbone for a range of tasks. This paper shows that Mamba's visual capability can be significantly enhanced through autoregressive pretraining, a direction not previously explored. Efficiency-wise, the autoregressive nature can well capitalize on the Mamba's unidirectional recurrent structure, enabling faster overall training speed compared to other training strategies like mask modeling. Performance-wise, autoregressive pretraining equips the Mamba architecture with markedly higher accuracy over its supervised-trained counterparts and, more importantly, successfully unlocks its scaling potential to large and even huge model sizes. For example, with autoregressive pretraining, a base-size Mamba attains 83.2% ImageNet accuracy, outperforming its supervised counterpart by 2.0%; our huge-size Mamba, the largest Vision Mamba to date, attains 85.0% ImageNet accuracy (85.5% when finetuned with 384×384 inputs), notably surpassing all other Mamba variants in vision. The code is available at urlhttps://github.com/OliverRensu/ARM.