SSM Adapters via Hankel Reduced-order Modeling: Injection Site Determines Task Suitability in Long-Context Fine-Tuning
arXiv:2606.26290v1 Announce Type: new Abstract: While parameter-efficient fine-tuning (PEFT) typically targets attention projectors, its efficacy for tasks requiring sequential state accumulation remains under-explored. We examine if PEFT for such tasks can benefit from state space model (SSMs) adapters, and if MLP blocks are better injection sites. We introduce Hankel Reduced order Model (HRM) adapter, an SSM-based residual module initialized via Balanced Truncation of empirical Hankel Grammians. By leveraging the time-invariance of the system matrix $\bar{A}$, HRM enables an exact FFT-based
The continuous push for more efficient and performant AI models, especially in long-context scenarios, drives innovation in fine-tuning techniques like SSM adapters.
Advanced PEFT methods utilizing SSMs could significantly enhance the capability and efficiency of large language models for complex, sequential tasks, reducing compute requirements for frontier AI development.
The exploration of SSM-based adapters and their optimal injection sites shifts the focus of PEFT research beyond traditional attention mechanisms, potentially enabling new architectural optimizations.
- · AI model developers
- · Cloud AI providers
- · Researchers in efficient AI
- · Inefficient AI fine-tuning methods
- · Hardware providers unprepared for new architectural demands
Improved performance and reduced computational cost for long-context AI applications like scientific research or complex code generation.
Faster iteration cycles and lower barriers to entry for developing and fine-tuning large AI models.
Accelerated deployment of highly specialized AI agents capable of handling extensive, context-rich tasks with greater accuracy and efficiency.
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