  Large multi-modal models (LMMs) exhibit remarkable performance across
numerous tasks. However, generalist LMMs often suffer from performance
degradation when tuned over a large collection of tasks. Recent research
suggests that Mixture of Experts (MoE) architectures are useful for instruction
tuning, but for LMMs of parameter size around O(50-100B), the prohibitive cost
of replicating and storing the expert models severely limits the number of
experts we can use. We propose Omni-SMoLA, an architecture that uses the Soft
MoE approach to (softly) mix many multimodal low rank experts, and avoids
introducing a significant number of new parameters compared to conventional MoE
models. The core intuition here is that the large model provides a foundational
backbone, while different lightweight experts residually learn specialized
knowledge, either per-modality or multimodally. Extensive experiments
demonstrate that the SMoLA approach helps improve the generalist performance
across a broad range of generative vision-and-language tasks, achieving new
SoTA generalist performance that often matches or outperforms single
specialized LMM baselines, as well as new SoTA specialist performance.
