To what extent does Uni-MoE-2.0-Omni's iterative reinforcement strategy improve reasoning accuracy on mathemat

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

To what extent does Uni-MoE-2.0-Omni's iterative reinforcement strategy improve reasoning accuracy on mathematical and code-generation tasks (e.g., GSM8K, HumanEval) compared to a standard supervised fine-tuning baseline, measured via pass@1 and majority-vote scores?

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