FLAME-MoE: A Transparent End-to-End Research Platform for Mixture-of-Experts Lan

Abstract

Abstract: Recent large language models such as Gemini-1.5, DeepSeek-V3, and Llama-4 increasingly adopt Mixture-of-Experts (MoE) architectures, which offer strong efficiency-performance trade-offs by activating only a fraction of the model per token. Yet academic researchers still lack a fully open, end-to-end MoE platform for investigating scaling, routing, and expert behavior. We release FLAME-MoE, a completely open-source research suite composed of seven decoder-only models, ranging from 38M to 1.7B active parameters, whose architecture–64 experts with top-8 gating and 2 shared experts–closely refle

Research Question

What is the impact of token scheduling in ExpertFlow on attribute binding accuracy (e.g., on AMBER) relative to dense baselines under varying expert activation budgets in MoE vision-language models?

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Provenance