How Close Is Encoder-Free Multimodal Pretraining to Dropping the Visual Encoder?
multimodal training
| Source: HF Papers | Original article
Researchers explore scaling laws for encoder‑free multimodal pretraining, assessing how close we are to eliminating visual encoders in large language models.
A new scaling‑laws study has quantified how multimodal large language models (MLLMs) behave when the traditional visual encoder is removed. The paper, authored by Lin Chen, Bolin Ni and Qi Yang, compares encoder‑free models that learn directly from raw pixels with the more common encoder‑based variants that rely on a pretrained visual backbone.
The analysis shows three key patterns. First, the compute‑optimal split for the multimodal training objective shifts toward larger model sizes once the visual encoder is omitted, while the allocation for the text‑only objective remains essentially unchanged. Second, at modest compute budgets encoder‑free systems fall behind their encoder‑based peers, but the gap narrows as training FLOPs increase. Third, the two families are projected to converge around 10^22 training FLOPs, a scale at which encoder‑free models would match the performance of encoder‑based ones.
Why this matters is twofold. Removing the visual encoder simplifies the architecture, eliminating the need for a separate pretrained vision component and potentially reducing engineering overhead. At the same time, the findings give researchers a concrete roadmap for budgeting compute: to reap the benefits of a unified pixel‑to‑language pipeline, they must invest in substantially larger models.
The next step will be to test the predictions empirically. As compute resources grow, teams are likely to launch training runs that approach the 10^22‑FLOP threshold, checking whether the projected catch‑up materialises in practice. Observers will watch for any shifts in corporate roadmaps—particularly among firms that currently build multimodal products on vision encoders—and for hardware‑software stacks that can support the larger models required for encoder‑free pretraining.
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