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 layer for steering varies substantially between inputs. Motivated by these observations, we introduce \textit{Where to Steer} (W2S), a general framework that adaptively selects intervention layers conditioned on the inputs by learning a mapping from input representations to steering layers. Across multiple LLMs and alignment behaviors, W2S consistently outperforms fixed-layer baselines in both in-distribution and out-of-distribution settings. Our findings suggest that input-dependent layer selection is a key design dimension complementary to methods for steering vector extraction.

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