Route Experts by Sequence, not by Token
📰 ArXiv cs.AI
SeqTopK routing method assigns experts based on sequence complexity, not token complexity, for more efficient large language models
Action Steps
- Identify sequence complexity to determine the number of experts needed
- Apply SeqTopK routing to assign experts based on sequence complexity
- Evaluate model performance and adjust SeqTopK parameters as needed
- Compare SeqTopK with standard TopK routing and other adaptive routing methods to determine its effectiveness
Who Needs to Know This
AI engineers and researchers working on large language models can benefit from this method to improve model efficiency and scalability, while software engineers can apply this concept to optimize system performance
Key Insight
💡 Assigning experts based on sequence complexity can lead to more efficient and scalable large language models
Share This
💡 Route experts by sequence, not token, for more efficient LLMs #LLMs #MixtureOfExperts
Key Takeaways
SeqTopK routing method assigns experts based on sequence complexity, not token complexity, for more efficient large language models
Full Article
Title: Route Experts by Sequence, not by Token
Abstract:
arXiv:2511.06494v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of experts to all tokens, ignoring their varying complexity. Prior adaptive routing methods introduce additional modules and hyperparameters, often requiring costly retraining from scratch. We propose Sequence-level TopK (SeqTopK), a minimal modification tha
Abstract:
arXiv:2511.06494v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) architectures scale large language models (LLMs) by activating only a subset of experts per token, but the standard TopK routing assigns the same fixed number of experts to all tokens, ignoring their varying complexity. Prior adaptive routing methods introduce additional modules and hyperparameters, often requiring costly retraining from scratch. We propose Sequence-level TopK (SeqTopK), a minimal modification tha
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