Training the image part from scratch can match models that used a separately trained image encoder, but results depend on data and
A careful, controlled test shows that dedicating vision capacity matters more than relying on pretrained vision parts, with results depending on data and compute.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Ellis Brown, Chris Hoang, Shengyi Qian, Shang-Wen Li, Mike Lewis, Marjan Ghazvininejad, Luke Zettlemoyer, Lucas Beyer, Mengye Ren, Jakob Verbeek, Saining Xie, Hu Xu
- Universities and research institutions
- New York University, Meta FAIR
Institution metadata: Original paper ↗
What they did and found
In a controlled test, researchers built multi-part models from the ground up using common data and the same total compute. They found the image part trained together with the rest could match or beat models that used a separately trained image encoder.
Why it matters
For organizations, this suggests you can save time and compute by training the image and language parts together, but outcomes depend on data and budget.
The study tested three design choices at once: training from scratch, a dedicated image-capability part, and where the image work sits in the model. It found the dedicated image-capability part was the key to better understanding, even when starting from scratch.
A practical takeaway is to start training image and language parts together early, and not rely on a large pretrained image encoder. Results can shift with data and compute limits.
Original sources · 1
- Unbundling Multimodal Vision Encoding ↗arXiv · 2026-11-12
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
The results apply to backbones around 1.7B and 4B parameters and the specific data mix used; different data or budgets may change outcomes.
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