Scaling Laws for Native Multimodal Models

Abstract

Abstract: Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In this work, we revisit the architectural design of native multimodal models (NMMs)-those trained from the ground up on all modali-ties-and conduct an extensive sca

Research Question

Does SMoES's soft modality-guided routing improve MoE-VLM accuracy on the MMMU benchmark compared to dense models of equivalent total parameter count, and how does this gap change when scaling from 7B to 34B total parameters?

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Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance