Where to Steer: Input-Dependent Layer Selection for Steering Improves LLM Alignment
Steering vectors have emerged as a lightweight and effective approach for aligning large language models (LLMs) at inference time, enabling modulation of model behaviors by shifting intermediate representations toward a target behavior. Many existing steering methods apply interventions at globally fixed locations in an LLM, effectively treating the optimal intervention location as input-invariant. However, representations relevant to a target behavior can be encoded at different LLM layers depending on the input. Theoretically, we show that different inputs may require steering at different layers to achieve alignment with a given behavior. Empirically, we observe that the most effective...
| Authors | Soham Gadgil, Chris Lin, Su-In Lee |
|---|---|
| Primary category | cs.LG |
| Published | 4 April 2026 |
| Last updated at source | 15 October 2026 |
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