Hybrid Architectures for Language Models: Systematic Analysis and Design
  Insights
Recent progress in large language models demonstrates that hybrid
architectures--combining self-attention mechanisms with structured state space
models like Mamba--can achieve a compelling balance between modeling quality
and computational efficiency, particularly for long-context tasks. While these
hybrid models show promising performance, systematic comparisons of
hybridization strategies and analyses on the key factors behind their
effectiveness have not been clearly shared to the community. In this work, we
present a holistic evaluation of hybrid architectures based on inter-layer
(sequential) or intra-layer (parallel) fusion. We evaluate these designs from a
variety of perspectives: language modeling performance, long-context
capabilities, scaling analysis, and training and inference efficiency. By
investigating the core characteristics of their computational primitive, we
identify the most critical elements for each hybridization strategy and further
propose optimal design recipes for both hybrid models. Our comprehensive
analysis provides practical guidance and valuable insights for developing
hybrid language models, facilitating the optimization of architectural
configurations.
68e47d99e4e093a7044e4d1d
Sangmin Bae
claimed_verified
2025-10-07T12:27:08.113Z
6602ca1e10a1441af41637be
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Sangmin Bae
user
raymin0223
68e47d99e4e093a7044e4d1e
Bilge Acun
68e47d99e4e093a7044e4d1f
Haroun Habeeb
68e47d99e4e093a7044e4d20
Seungyeon Kim
68e47d99e4e093a7044e4d21
Chien-Yu Lin
68e47d99e4e093a7044e4d22
Liang Luo
68e47d99e4e093a7044e4d23
Junjie Wang
68e47d99e4e093a7044e4d24
Carole-Jean Wu
Hybrid Architectures for Language Models: Systematic Analysis and Design
  Insights
Recent progress in large language models demonstrates that hybrid
architectures--combining self-attention mechanisms with structured state space
models like Mamba--can achieve a compelling balance between modeling quality
and computational efficiency, particularly for long-context tasks. While these
hybrid models show promising performance, systematic comparisons of
hybridization strategies and analyses on the key factors behind their
effectiveness have not been clearly shared to the community. In this work, we
present a holistic evaluation of hybrid architectures based on inter-layer
(sequential) or intra-layer (parallel) fusion. We evaluate these designs from a
variety of perspectives: language modeling performance, long-context
capabilities, scaling analysis, and training and inference efficiency. By
investigating the core characteristics of their computational primitive, we
identify the most critical elements for each hybridization strategy and further
propose optimal design recipes for both hybrid models. Our comprehensive
analysis provides practical guidance and valuable insights for developing
hybrid language models, facilitating the optimization of architectural
configurations.
A comprehensive evaluation of hybrid language models combining self-attention with structured state space models, analyzing inter-layer and intra-layer fusion strategies, and providing design recommendations.
self-attention mechanisms structured state space models Mamba hybrid architectures inter-layer fusion intra-layer fusion language modeling performance long-context capabilities scaling analysis training efficiency inference efficiency computational primitive