Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced M

Abstract

Abstract: We present Uni-MoE 2.0 from the Lychee family. As a fully open-source omnimodal large model (OLM), it substantially advances Lychee's Uni-MoE series in language-centric multimodal understanding, reasoning, and generating. Based on the dense LLM, we build Uni-MoE-2.0-Omni from scratch through three core contributions: dynamic-capacity Mixture-of-Experts (MoE) design, a progressive training strategy enhanced with an iterative reinforcement strategy, and a carefully curated multimodal data matching technique. It is capable of omnimodal understanding, as well as generating images, text, and speech

Research Question

How does the computational efficiency (FLOPs per forward pass) of SMoES scale with the number of active experts and input modality composition relative to dense and hard-routed MoE baselines across different batch sizes on multimodal QA tasks?

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