Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs
Authors: name: Pengrui Han, name: Xueqiang Xu, name: Keyang Xuan, name: Peiyang Song, name: Siru Ouyang, name: Runchu Tian, name: Yuqing Jiang, name: Cheng Qian, name: Pengcheng Jiang, name: Jiashuo Sun, name: Junxia Cui, name: Ming Zhong, name: Ge Liu, name: Jiawei Han, name: Jiaxuan You
Organization: University of Illinois at Urbana-Champaign

Activation steering has emerged as a promising approach for efficiently adapting large language models (LLMs) to downstream behaviors. However, most existing steering methods rely on a single static direction per task or concept, making them inflexible under task variation and inadequate for complex tasks that require multiple coordinated capabilities. To address this limitation, we propose STEER2ADAPT, a lightweight framework that adapts LLMs by composing steering vectors rather than learning new ones from scratch. In many domains (e.g., reasoning or safety), tasks share a small set of underlying concept dimensions. STEER2ADAPT captures these dimensions as a reusable, low-dimensional semantic prior subspace, and adapts to new tasks by dynamically discovering a linear combination of basis vectors from only a handful of examples. Experiments across 9 tasks and 3 models in both reasoning and safety domains demonstrate the effectiveness of STEER2ADAPT, achieving an average improvement of 8.2%. Extensive analyses further show that STEER2ADAPT is a data-efficient, stable, and transparent inference-time adaptation method for LLMs.