Hybrid Architectures for Language Models: Systematic Analysis and Design Insights
Sangmin Bae, Bilge Acun, Haroun Habeeb, Seungyeon Kim, Chien-Yu Lin, Liang Luo, Junjie Wang, Carole-Jean Wu
2025-10-07
Summary
This paper investigates how to best combine two different types of building blocks for large language models: self-attention (like in ChatGPT) and a newer approach called structured state space models (specifically, Mamba). The goal is to create models that are both powerful and efficient, especially when dealing with very long pieces of text.
What's the problem?
While combining these two approaches seems promising, it wasn't clear *how* to best combine them. Researchers hadn't systematically compared different ways of mixing self-attention and Mamba, or figured out exactly *why* some combinations work better than others. This lack of understanding made it hard to build the most effective hybrid models.
What's the solution?
The researchers thoroughly tested two main strategies for combining these technologies: one where they layered them sequentially (one after the other) and one where they used them in parallel (at the same time). They looked at how well these different designs performed on language tasks, how they handled long texts, how their performance changed as the model size increased, and how efficiently they could be trained and used. By analyzing the core characteristics of each approach, they identified the most important factors for success and developed guidelines for building optimal hybrid models.
Why it matters?
This work provides practical advice for anyone designing new large language models. It helps developers understand which combination strategies are most effective and how to configure the models for the best performance and efficiency, ultimately speeding up progress in the field and enabling better models for handling long and complex information.
Abstract
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.