State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for
  State Space Models
Authors: name: Wonjun Kang, name: Kevin Galim, name: Yuchen Zeng, name: Minjae Lee, name: Hyung Il Koo, name: Nam Ik Cho

State Space Models (SSMs) have emerged as efficient alternatives to
Transformers, mitigating their quadratic computational cost. However, the
application of Parameter-Efficient Fine-Tuning (PEFT) methods to SSMs remains
largely unexplored. In particular, prompt-based methods like Prompt Tuning and
Prefix-Tuning, which are widely used in Transformers, do not perform well on
SSMs. To address this, we propose state-based methods as a superior alternative
to prompt-based methods. This new family of methods naturally stems from the
architectural characteristics of SSMs. State-based methods adjust state-related
features directly instead of depending on external prompts. Furthermore, we
introduce a novel state-based PEFT method: State-offset Tuning. At every
timestep, our method directly affects the state at the current step, leading to
more effective adaptation. Through extensive experiments across diverse
datasets, we demonstrate the effectiveness of our method. Code is available at
https://github.com/furiosa-ai/ssm-state-tuning.