How does the MambaFormer hybrid MoE architecture's efficiency (FLOPs per token and throughput) scale with mode

Abstract

Abstract: Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, arc

Research Question

How does the MambaFormer hybrid MoE architecture's efficiency (FLOPs per token and throughput) scale with model width and number of experts on code generation tasks using HumanEval and MBPP, relative to standard Transformer MoE models?

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Quality Dimensions

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Correction Record

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Provenance