State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for
  State Space Models
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.
67cb02680a2a716f25805cb4
Wonjun Kang
admin_assigned
2025-03-11T14:54:53.750Z
63e1d5247fbb6ae4d4f4cc8e
/avatars/8a8f700adf9e8000641c2c2f6bd56080.svg
Wonjun Kang
user
wjkang
67cb02680a2a716f25805cb5
Kevin Galim
admin_assigned
2025-03-11T14:55:00.348Z
630c90123dc31beba6e8f406
/avatars/2188b41fff122d4f5683b46c529ed79d.svg
Kevin Galim
user
kev95
67cb02680a2a716f25805cb6
Yuchen Zeng
admin_assigned
2025-03-11T14:55:07.129Z
65ded6ec6efc2a4ebb18d306
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/SPsUBPd_CKMBzACSCgwP5.png
Yuchen Zeng
user
yzeng58
67cb02680a2a716f25805cb7
Minjae Lee
claimed_verified
2025-08-26T09:51:24.683Z
6788cbfd6cdca10c9bb3dea5
/avatars/a0084b72d5b1a82d9c8a5aef1a230ffd.svg
Minjae Lee
user
FuriosaMJLEE
67cb02680a2a716f25805cb8
Hyung Il Koo
67cb02680a2a716f25805cb9
Nam Ik Cho
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for
  State Space Models
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.
State-based PEFT methods, including State-offset Tuning, improve the adaptability and efficiency of State Space Models more effectively than prompt-based methods.
State Space Models Parameter-Efficient Fine-Tuning Prompt Tuning Prefix-Tuning State-offset Tuning