Towards Physics of Multimodal Pretraining: Knowledge Flow, Modality Synergy, Early Unification, and Recipes

2026-08-05

AI Summary

This work systematically investigates the core mechanisms of modality interaction in multimodal pretraining, focusing on knowledge transfer, synergistic effects, and fusion timing. Through experiments on both synthetic and large-scale real-world datasets, it provides the first empirical evidence of asymmetric cross-modal knowledge flow and demonstrates that data complexity governs whether modalities exhibit synergy or competition. The study further validates that early unified fusion consistently outperforms late alignment. Leveraging an architecture featuring shared attention and normalization layers with modality-specific feedforward components, the proposed approach is evaluated on a 13.5B mixture-of-experts model trained on 2 trillion tokens, confirming its effectiveness. Additionally, the paper introduces a highly efficient pretraining strategy that achieves strong generative performance using only 5% of the typical computational budget.

Abstract

Vision offers a critical axis for advancing foundation models, driving a shift towards natively unified multimodal pretraining. Despite this momentum, the design space and the fundamental mechanisms of how modalities interact during unified training remain underexplored. We provide empirical clarity through a systematic exploration of multimodal pretraining. Our controlled experiments on both synthetic and large-scale real-world datasets yield four key insights into the physics of multimodal pretraining: (i) Knowledge Flow: We disentangle how language, visual understanding, and visual generation transfer knowledge across modalities, revealing distinct patterns of influence and asymmetry; (ii) Synergy vs. Competition: We show that data "complexity" largely determines whether modalities are synergistic, identify architectural choices that promote synergy: such as shared attention and normalization with modality-specific feed-forward layers, and find that these behaviors generalize across different visual tokenizer designs; (iii) Early Unification: Unifying modalities from the very early stages and training them jointly is shown to be more effective than late alignment or sequential training. This process uncovers a vision laziness phenomenon, where delayed integration leads models to rely on language priors; (iv) Recipes: We derive efficient pretraining recipes that achieve strong generative performance using only 5% of the compute budget. These core findings are subsequently validated at scale by training multiple 13.5B MoE models on 2T tokens. We hope this study provides a principled foundation for understanding and scaling multimodal pretraining.

Problem

• multimodal pretraining
• modality interaction
• knowledge flow
• modality synergy
• early unification

Innovation

• multimodal pretraining
• knowledge flow
• modality synergy
• early unification
• efficient recipes