# SOLO: A Single Transformer for Scalable Vision-Language Modeling

arXiv: https://arxiv.org/abs/2407.06438 · HTML: https://mlsanity.com/paper/2407.06438/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 110 of its 137 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 110)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning](https://mlsanity.com/paper/2305.06500/) (2023)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond](https://mlsanity.com/paper/2308.12966/) (2023)
- [MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI](https://mlsanity.com/paper/2311.16502/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)

## Built on by (most cited first, 30 of 37)

- [Mono-InternVL: Pushing the Boundaries of Monolithic Multimodal Large Language Models with Endogenous Visual Pre-training](https://mlsanity.com/paper/2410.08202/) (2024)
- [LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering](https://mlsanity.com/paper/2412.12359/) (2024)
- [Scaling Laws for Predicting Downstream Performance in LLMs](https://mlsanity.com/paper/2410.08527/) (2024)
- [MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding](https://mlsanity.com/paper/2411.17762/) (2024)
- [SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding](https://mlsanity.com/paper/2412.09604/) (2024)
- [EVEv2: Improved Baselines for Encoder-Free Vision-Language Models](https://mlsanity.com/paper/2502.06788/) (2025)
- [SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture](https://mlsanity.com/paper/2605.12500/) (2026)
- [The Scalability of Simplicity: Empirical Analysis of Vision-Language Learning with a Single Transformer](https://mlsanity.com/paper/2504.10462/) (2025)
- [Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey](https://mlsanity.com/paper/2412.18619/) (2024)
- [Pixel-SAIL: Single Transformer For Pixel-Grounded Understanding](https://mlsanity.com/paper/2504.10465/) (2025)
- [From Pixels to Words -- Towards Native Vision-Language Primitives at Scale](https://mlsanity.com/paper/2510.14979/) (2025)
- [Exploring the Potential of Encoder-free Architectures in 3D LMMs](https://mlsanity.com/paper/2502.09620/) (2025)
- [Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models](https://mlsanity.com/paper/2507.12566/) (2025)
- [Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation](https://mlsanity.com/paper/2502.13145/) (2025)
- [HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding](https://mlsanity.com/paper/2412.16158/) (2024)
- [Dense360: Dense Understanding from Omnidirectional Panoramas](https://mlsanity.com/paper/2506.14471/) (2025)
- [LLaVA-UHD v2: an MLLM Integrating High-Resolution Semantic Pyramid via Hierarchical Window Transformer](https://mlsanity.com/paper/2412.13871/) (2024)
- [Breaking the Encoder Barrier for Seamless Video-Language Understanding](https://mlsanity.com/paper/2503.18422/) (2025)
- [LLaVA-SP: Enhancing Visual Representation with Visual Spatial Tokens for MLLMs](https://mlsanity.com/paper/2507.00505/) (2025)
- [BREEN: Bridge Data-Efficient Encoder-Free Multimodal Learning with Learnable Queries](https://mlsanity.com/paper/2503.12446/) (2025)
- [NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints](https://mlsanity.com/paper/2510.08565/) (2025)
- [Decoupling Contrastive Decoding: Robust Hallucination Mitigation in Multimodal Large Language Models](https://mlsanity.com/paper/2504.08809/) (2025)
- [NarrativeTrack: Evaluating Entity-Centric Reasoning for Narrative Understanding](https://mlsanity.com/paper/2601.01095/) (2026)
- [Verbalized Representation Learning for Interpretable Few-Shot Generalization](https://mlsanity.com/paper/2411.18651/) (2024)
- [Prioritizing Image-Related Tokens Enhances Vision-Language Pre-Training](https://mlsanity.com/paper/2505.08971/) (2025)
- [Unleashing the Intrinsic Visual Representation Capability of Multimodal Large Language Models](https://mlsanity.com/paper/2512.06281/) (2025)
- [Point Cloud as a Foreign Language for Multi-modal Large Language Model](https://mlsanity.com/paper/2603.09173/) (2026)
- [Let ViT Speak: Generative Language-Image Pre-training](https://mlsanity.com/paper/2605.00809/) (2026)
- [Enhancing Self-Supervised Visual Representation Learning via Low-Rank Adapted LLMs](https://mlsanity.com/paper/2507.00754/) (2025)
- [VAT: Vision Action Transformer by Unlocking Full Representation of ViT](https://mlsanity.com/paper/2512.06013/) (2025)

## Abstract (arXiv)

We present SOLO, a single transformer for Scalable visiOn-Language mOdeling. Current large vision-language models (LVLMs) such as LLaVA mostly employ heterogeneous architectures that connect pre-trained visual encoders with large language models (LLMs) to facilitate visual recognition and complex reasoning. Although achieving remarkable performance with relatively lightweight training, we identify four primary scalability limitations: (1) The visual capacity is constrained by pre-trained visual encoders, which are typically an order of magnitude smaller than LLMs. (2) The heterogeneous architecture complicates the use of established hardware and software infrastructure. (3) Study of scaling laws on such architecture must consider three separate components - visual encoder, connector, and LLMs, which complicates the analysis. (4) The use of existing visual encoders typically requires following a pre-defined specification of image inputs pre-processing, for example, by reshaping inputs to fixed-resolution square images, which presents difficulties in processing and training on high-resolution images or those with unusual aspect ratio. A unified single Transformer architecture, like SOLO, effectively addresses these scalability concerns in LVLMs; however, its limited adoption in the modern context likely stems from the absence of reliable training recipes that balance both modalities and ensure stable training for billion-scale models. In this paper, we introduce the first open-source training recipe for developing SOLO, an open-source 7B LVLM using moderate academic resources. The training recipe involves initializing from LLMs, sequential pre-training on ImageNet and web-scale data, and instruction fine-tuning on our curated high-quality datasets. On extensive evaluation, SOLO demonstrates performance comparable to LLaVA-v1.5-7B, particularly excelling in visual mathematical reasoning.