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This article introduces the EMO framework, which achieves natural modular grouping of experts through document-level expert pool constraints. It allows MoE models to lose only 1% performance when using just 25% of experts, breaking through the modular bottleneck of traditional MoE.
Section 01
This article introduces the EMO framework, which achieves natural modular grouping of experts through document-level expert pool constraints. It allows MoE models to lose only 1% performance when using just 25% of experts, breaking through the modular bottleneck of traditional MoE and solving the practical dilemma of being unable to flexibly prune parameters.
Section 02
Mixture-of-Experts (MoE) models theoretically reduce inference costs through sparse activation, but in reality, they lack true modularity: performance drops sharply when fixing a subset of domain-specific experts, and deployment still requires loading all parameters, which goes against the original intention of sparse design.
Section 03
Core insight of EMO: Tokens from the same document tend to select the same subset of experts. Implementation constraints: Shared expert pool within a document, independent selection between documents; Technical details: Overlapping expert pool division, token-level routing with pool constraints, standard language modeling pre-training objective (no additional loss).
Section 04
Comparison of models pre-trained on 1T tokens (1B active parameters /14B total parameters):
Section 05
The modular characteristics of EMO bring new deployment possibilities:
Section 06
Technical insights: Simple constraints are better than complex designs, leveraging emergent behaviors, unifying structure and function; Future directions: Fine-grained expert pool division, research on expert dependency relationships, multimodal expansion.
Section 07
EMO achieves truly modular MoE through document-level constraints, gaining practical pruning capability for the first time, improving deployment flexibility, and opening a new path for building composable and scalable large-scale AI systems.
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