LayerNavigator: Finding Promising Intervention Layers for Efficient Activation Steering in Large Language Models

We sincerely appreciate the reviewer’s insightful comments and recognition of our method’s efficiency and empirical rigor. Below, we provide detailed responses to the specific questions raised.

Q1: Are the selected layers complementary in function or position?

Thank you for highlighting this point. Our experiments consistently show that the most steerable layers cluster around the middle and latter parts of the model. This trend holds across tasks, extraction methods, and model scales.

Interestingly, similar patterns have been observed in other recent work. For instance, Semantic-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors (Wang et al., ICLR 2025) and How do large language models handle multilingualism? (Zhao et al., NeurIPS 2024) both highlight the importance of middle and latter layers for behavior modulation and language control.

While a complete theoretical explanation remains open, one widely discussed hypothesis is the functional segregation hypothesis—which suggests that middle layers are responsible for abstract reasoning and decision-making, while latter layers specialize in surface-level generation. Our findings offer empirical support for this perspective from the lens of layer-wise steerability.

Q2: Interpreting steerability scores under different extraction methods (MD vs. PCA)

As discussed in Section 4.7, the steerability score is a relative metric within each method, not directly comparable across different extraction algorithms. This distinction arises from the fundamental difference in how steering vectors are constructed:

• MD (Mean Difference) relies on pairwise contrastive comparisons, emphasizing local semantic shifts between positive and negative prompts.
• PCA captures global variance directions across all prompts, potentially smoothing over noise but also capturing more general latent structure.

In our observation, MD tends to be more effective when the original alignment probability is low, as it extracts sharp behavioral differences. Conversely, PCA performs better when alignment is already high, where finer-grained distinctions can be more reliably extracted from the dense feature space.

We agree that more principled guidance for method selection would be valuable and consider this an important direction for future work.

Q3: Dynamic selection of $K$ based on steerability scores

This is an excellent question. One technical constraint is that we apply z-score normalization to the activations before computing steerability scores (Eq. 6). This step is critical: without normalization, later layers—which naturally have higher activation magnitudes—would dominate the scores unfairly.

However, as a consequence, the resulting scores are not comparable across tasks, which limits their utility for determining a universal cutoff or threshold on $K$.

To empirically address this issue, we conducted extensive experiments on all 135 persona behaviors in the Anthropic dataset.

The summary below reports average alignment probabilities across different $K$ and $\alpha$:

We also measured how often each method achieved peak performance when $K=7$:

In practice, we recommend using larger $K$ (e.g., 5–7) with smaller $\alpha$ (e.g., 0.5) to obtain stable and interpretable steering behavior.

Q4: Addressing Remaining Concerns in Weaknesses

We appreciate the reviewer’s suggestion to further clarify the practical value of LayerNavigator relative to ground-truth and broader task coverage. Below, we address both aspects in turn.

On Ground-Truth Layer Selection and Cost

To approximate an upper bound, we performed grid search over all $\binom{32}{2}=496$ layer pairs ($K=2$) on the six primary tasks. The best combination per task was selected and compared with our method as well as the strongest baselines across all $K=1$ to $K=7$ settings.

Notably, LayerNavigator outperforms even the grid search baseline on most tasks despite the latter having access to full evaluation feedback. This suggests our method is not just efficient, but also highly competitive.

We also report wall-clock runtimes measured on a standardized cloud instance (20 vCPUs,Intel(R) Xeon(R) Platinum 8457C and a single NVIDIA L20 GPU, 48GB):

Thus, LayerNavigator achieves these strong results with <0.3% of the computation required by exhaustive grid search—underscoring its practical scalability for real-world use.

Generalization to Non-Persona Tasks

To test generalizability, we evaluate on two non-persona alignment benchmarks—Refusal and Hallucination—as proposed by “Steering Llama 2 via Contrastive Activation Addition” (Rimsky et al., ACL 2024). These tasks represent distinct behavioral domains, such as harmful content refusal and factual consistency.

Across a range of $K$ and $\alpha$ values, LayerNavigator delivers strong and stable alignment:

Refusal

Hallucination

These results demonstrate that LayerNavigator remains effective beyond the persona setting, providing a reliable layer selection mechanism across diverse alignment tasks.

We sincerely thank the reviewer for the encouraging and thoughtful feedback. We are especially grateful for your recognition of the conceptual clarity and practical relevance of our proposed method. Below, we provide detailed responses to the raised concerns.

Q1: More Insights into Why Middle and Later Layers Are Steerable

You raise an excellent point about the need for deeper theoretical understanding of layer-wise steerability. Empirically, we observe that the middle and later layers of LLMs tend to yield better behavioral alignment—an observation corroborated by our steerability score and performance metrics.

This phenomenon has also been reported in other recent studies. For instance, Semantic-Adaptive Activation Intervention for LLMs via Dynamic Steering Vectors (Wang et al., ICLR 2025) and How do large language models handle multilingualism? (Zhao et al., NeurIPS 2024) both note that the middle and latter layers are more influential in behavior modulation and representation formation.

One widely discussed explanation is the functional segregation hypothesis, which posits that middle layers encode abstract reasoning and decision-making, while latter layers are more associated with surface-level generation. While our paper does not aim to resolve this question definitively, our statistical and experimental findings provide supporting evidence for this perspective from a new angle.

We agree that further probing of functional roles is an exciting direction, and we hope our steerability metric can serve as a tool for such deeper analysis.

Q2: Evaluation Beyond Persona Alignment

To better assess generality, we expanded our evaluation to two non-persona alignment tasks—Refusal and Hallucination—based on benchmarks proposed by “Steering Llama 2 via Contrastive Activation Addition” (Rimsky et al., ACL 2024). The results below show that LayerNavigator remains competitive with baselines under various values of $K \in$ {$1,2,3,4,5,6,7$} and $\alpha \in$ {$0.5,1.0,1.5$}.

Refusal

Hallucination

We also tested LayerNavigator on all 135 persona behaviors in the Anthropic Model-Written Evaluations (MWE). The summary below reports average alignment probabilities across different $K$ and $\alpha$:

Summary Statistics Compared to Top, LayerNavigator:

• Outperforms on 23% of tasks
• Within 1.0% on 47% of tasks
• Within 2.0% on 65%

Compared to Around Top:

• Outperforms on 30%
• Within 1.0% on 51%
• Within 2.0% on 69%

While LayerNavigator may trail the top-performing configuration by ~1–2% on average, it avoids the high variance and instability observed in Top and Around Top baselines under aggressive steering. Crucially, it requires no held-out evaluation, making it 20× more efficient and highly suitable for large-scale and resource-constrained applications.

Finally, we highlight the average runtime across six main tasks to underscore LayerNavigator’s efficiency:

Q3: On the Role of $K$ in Multi-Layer Steering

We appreciate your attention to the effect of the number of intervention layers ($K$), which is indeed a critical hyperparameter in activation steering.

By aggregating results across the 135 persona tasks, we observe two key trends:

• Both $K$ and $\alpha$ exhibit non-linear effects—alignment performance increases up to an optimal value, after which further increase may introduce noise or oversteering.
• Larger $\alpha$ can compensate for smaller $K$, and vice versa. However, the best combination varies across tasks, reinforcing the need for tunable and stable methods.

We also measured how often each method achieved peak performance when $K=7$:

These trends suggest that LayerNavigator scales better with increasing $K$, providing practical flexibility for real-world applications. We plan to incorporate these extended analyses in the final version.

Q4: Evaluation on More Model Scales

Thank you for pointing out the importance of evaluating across model scales. To this end, we report additional results on Qwen-2.5-0.5B-Instruct and Qwen-2.5-7B-Instruct, using MD extraction and $\alpha = 1.0$. For each method, we report the best alignment probability across $K \in$ {$1, 2, 3, 4, 5$}.

Qwen-2.5-0.5B-Instruct

Qwen-2.5-7B-Instruct

These results affirm that LayerNavigator remains effective across smaller (0.5B) and larger (7B) models, and even slightly improves over baselines in most tasks.

When examining the selected layers, we again find that steerable layers consistently fall into the middle and later regions of the model—reinforcing the generality of the observations discussed in Q1 across model scales.

Thank you for your constructive feedback. We address your concerns below.

Q1: Ablation Study

In Section 4.5 and Figure 5, we vary the relative weights of discriminability and consistency, observing a concave (∩-shaped) trend peaking near 50:50, which supports the importance of both terms.

While endpoints (0:100, 100:0) were not shown, we agree they represent standard ablation settings. We have now included those results in the new version of the manuscript, confirming that using either term alone leads to worse alignment performance.

Q2: Computational Cost

We conducted our experiments on a cloud platform equipped with 20 vCPUs (Intel(R) Xeon(R) Platinum 8457C) and a single NVIDIA L20 GPU (48GB). Below is a summary of the average runtime across the six behavior alignment tasks used in our study:

Practical steering requires selecting three key hyperparameters: (1) the steering strength $\alpha$, (2) the number of layers $K$, and (3) the specific layer combination $S$. While $\alpha$ and $K$ can be efficiently tuned via simple grid search (due to their low dimensionality), searching for optimal layer combinations $S$ is combinatorially expensive.

In this context, LayerNavigator offers a substantial advantage by providing near-optimal (and sometimes optimal) layer selection outcomes, while Top and Around Top require more than 20× the compute time of LayerNavigator.

Q3: Evaluation on Non-Persona or Open-Ended Alignment Tasks

We appreciate your question regarding the generality of our method beyond the original persona-based tasks.

Clarifying “Open-Ended” Evaluation

Our current setup is already open-ended in a generative sense. Specifically, we prompt the model to answer “Yes” or “No” to a question, followed by free-form explanation generation, as shown in Figure 2. This setup enables automatic and scalable evaluation of both behavioral alignment (via token probability) and language quality (via perplexity of the explanation), providing a reliable assessment of the model’s performance after steering.

If “open-ended” refers to prompting models with unconstrained questions and relying on human or AI-based scoring, we agree such methods have merit but also suffer from reproducibility and subjectivity challenges.

Results on Non-Persona Tasks

To further verify generality, we apply LayerNavigator to two non-persona alignment benchmarks—Refusal and Hallucination—from “Steering Llama 2 via Contrastive Activation Addition” (Rimsky et al., ACL 2024). The following tables report alignment probability (%) across different values of $K$. We default to Mean Difference extraction in the following experiments. The best score per method is bolded.

Refusal

Hallucination

While our method slightly underperforms Top, it consistently exceeds Around Top 1 across both tasks, while reducing computational cost by over 20×. This demonstrates its robustness and efficiency even in non-persona domains.

Evaluation on 135 Full Persona Tasks

We also tested LayerNavigator on the complete set of 135 persona tasks from Anthropic’s Model-Written Evaluations. The table below reports the average alignment probability (%) of all tasks across different values of $K$. Bold indicates the best result per method.

Summary Statistics Compared to Top, LayerNavigator:

• Outperforms on 23% of tasks
• Within 1.0% on 47% of tasks
• Within 2.0% on 65%

Compared to Around Top:

• Outperforms on 30%
• Within 1.0% on 51%
• Within 2.0% on 69%

In summary, despite trailing top-performing baselines by a modest ~1–2% on average, LayerNavigator offers a far more favorable cost-performance trade-off considering its more than 20× lower computational cost.

Q4: Justification for the Choice of $K=1,3,5$

Thank you for raising this point. Our main experiments use $K=1,3,5$ based on the following considerations:

1. When $K>5$, Top and Around Top baselines begin to exhibit performance degradation, especially due to poor generalization when steering too many layers without principled selection.
2. We include $K=1$ specifically because Top achieves the theoretical upper bound under this setting (it selects the best-performing single layer via grid-search on the validation set). This establishes a reference point to assess multi-layer strategies.
3. While we initially planned to show results for all $K \in$ {$1, 2, 3, 4, 5$}, we chose $K=1,3,5$ for clarity and conciseness, believing this subset captures the full trend without redundancy.

As elaborated in Q3, our supplementary results reveal two more important trends:

• Both $K$ and $\alpha$ exhibit non-monotonic behavior—alignment performance improves up to a certain threshold, then deteriorates.
• $K$ and $\alpha$ can partially compensate for one another: smaller $K$ often benefits from larger $\alpha$, and vice versa. However, the optimal balance is task-dependent.

Q5: Clarifications on Additional Concerns Raised in the Weaknesses

1. Lack of Statistical Significance Analysis As is standard in activation steering work, our experiments use deterministic generation (i.e., do_sample=False) to ensure reproducibility. Thus, significance testing is not necessary.

   Besides, in Section 4.6, we evaluate robustness by randomly removing portions of the training set. To account for the variability introduced by this process, we conduct five independent trials and report the resulting variance with error bars in Appendix Figure 8.

2. "Minor Significance" of Gains As discussed in Q2, the primary contribution of LayerNavigator is not in raw improvement magnitude, but in achieving comparable performance to validation-intensive methods with negligible cost. We view this as a highly practical trade-off, especially under resource constraints.

3. "Question Marks on Originality" While the general idea of measuring activation quality is not new, our method is the first to systematically address layer selection for activation steering, using internal signals alone. Prior approaches, such as linear probing or causal tracing, aim at interpretability or attribution, and are not directly applicable to selecting layers. These methods also typically involve expensive procedures, e.g., training auxiliary classifiers or running thousands of intervention-based patches.

   In contrast, LayerNavigator leverages activations already computed during steering vector extraction and introduces a targeted metric (steerability score) grounded in discriminability and consistency. This direction is novel and valuable for the growing community using steering methods at scale.

4. Presentation Issues We sincerely appreciate your writing-related feedback and will revise the manuscript to clarify all definitions (e.g., steerability, consistency), clean up citation formatting, and move relevant implementation details into the main text. Your suggestions will help us significantly improve the clarity and accessibility of the work.

Thank you for the additional clarification; however, my assessment remains unchanged. The new data confirms my primary concerns: the method's performance gains are not stable across key hyperparameters like $K$ and $\alpha$, which undermines its reliability and suggests the favorable results may be cherry-picked. Also, the evaluation's significance is limited by its reliance on a brute-force grid search as a primary baseline; a more compelling case would require comparison against other intelligent, lightweight heuristics, not just the extremes of random chance and exhaustive search. Moreover, the runtime analysis is incomplete by focusing only on the selection step. A trustworthy efficiency claim requires a holistic analysis of the total workflow time, as the reported savings may be insignificant in the larger context of steering vector extraction and final model inference.

Thank you for the final clarification. However, your response further solidifies my concerns.

You are correct that costs like steering vector extraction are constant across methods, but you miss the fundamental point of the comparison. The most critical comparison is not only between LayerNavigator and a brute-force search, but also between LayerNavigator and simple baseline methods like direct generation, which have zero compute overhead at all. Your method introduces an additional time overhead relative to these baselines. Therefore, providing the vector extraction time (or to say, the time of conducting a whole pipeline on a certain test set) is crucial, as it allows for a proper cost-benefit analysis: Is the marginal performance gain from LayerNavigator worth its (marginal) time cost?

By focusing only on the speedup over brute-force and omitting the time context that would reveal the slowdown against simple baselines, your evaluation seems to be cherry-picking its comparisons. This lack of a holistic and transparent cost-benefit analysis against the most practical baselines is a fundamental flaw, and I just cannot recommend acceptance.

We sincerely thank the reviewer for the thoughtful and constructive feedback. We appreciate your recognition of the clarity and motivation behind our proposed method. Below, we address your concerns point by point.

Q1: Why are the “Top” baselines missing in the Qwen experiments?

Thank you for raising this concern. We now provide the full set of results on Qwen-2.5-32B-Instruct, including both the “Top” and “Around Top 1” baselines:

These results reveal that Top and Around Top exhibit notable instability—while they occasionally outperform our method (e.g., by 1–2% on Religion Following), they can also lead to severe degradation (e.g., dropping to ~50% on Allies Building).

Q2: Clarifying the “Top” baseline

We agree that the original description could be more precise. Here we clarify:

For the Top baseline, we compute steering vectors at all $L$ layers, then perform single-layer steering using each vector and evaluate the alignment probability on a held-out validation set. The top-$K$ layers are then selected based on their individual performance rankings, and this process requires $L \times N_{\text{eval}}$ additional forward passes.

Q3: On the Choice of $K$ and $\alpha$

Thank you for raising this important question. Unless otherwise specified, all experiments (e.g., Figures 4, 5, 6) were conducted with $K=5$ layers by default.

We now revisit the interaction between $K$ and $\alpha$ in greater depth, incorporating both the six main behaviors and our extended experiments over 135 persona-related tasks from the MWE (see Q4 below). Our findings are as follows:

• Both $K$ and $\alpha$ exhibit non-monotonic effects: alignment improves up to an optimal value and then degrades. Excessively large $\alpha$ or $K$ may cause oversteering or introduce noisy directions.

• There exists a compensatory relation: smaller $K$ often benefits from larger $\alpha$, and vice versa. This trade-off, however, is task-dependent and must be tuned accordingly.

• Across all 135 tasks, LayerNavigator selects $K=7$ as the best-performing layer count more frequently than the baselines:

These trends suggest that LayerNavigator scales better with increasing $K$, providing practical flexibility for real-world applications. We plan to incorporate these extended analyses in the final version.

Q4: Evaluation Scope on MWE

We appreciate your suggestion regarding a broader evaluation. In the initial draft, we heuristically selected six representative behaviors from the Model-Written Evaluations (MWE) dataset. To address this limitation, we have now extended our evaluation to cover all 135 persona-related behaviors in MWE.

The table below reports the average alignment probability across all tasks, under varying $K$ and $\alpha$ values. We default to Mean Difference extraction. Bold indicates the best result per method.

Summary Statistics Compared to Top, LayerNavigator:

• Outperforms on 23% of tasks
• Within 1.0% on 47% of tasks
• Within 2.0% on 65%

Compared to Around Top:

• Outperforms on 30%
• Within 1.0% on 51%
• Within 2.0% on 69%

While our method may trail the very best-performing baseline by ~1–2% on average, it does so with zero additional inference overhead and avoids the instability and brittleness observed in Top-based methods under aggressive steering settings. This makes LayerNavigator highly practical for scaling to large behavior spaces and model architectures.

Finally, we highlight the average runtime across six main tasks to underscore LayerNavigator’s efficiency:

Q6: On the Negative Effect of LayerNavigator in Self-Improvement Task when $K=1$

Thank you for pointing out this edge case. We re-ran the experiments for the Self-Improvement task with $K=1$ and confirmed that LayerNavigator selected layer 13 for steering, whereas the Top baseline (selected via grid-search on validation set) chose layer 16. We further verified that steering at layer 13 indeed leads to a negative effect even on the validation set, ruling out data noise as the cause.

To better understand how common such failure cases are, we analyzed all 32 layers across all 135 persona behaviors (i.e., 4320 steering vectors total). We found that 16.9% of vectors result in negative steering effects—i.e., they reduce the alignment probability below the base model. In contrast, the failure rate for LayerNavigator when $K=1$ is only 5.9%, indicating that it still selects relatively safe layers overall.

More importantly, our method truly shines when $K$ is larger. Because LayerNavigator reuses precomputed activations and does not rely on held-out evaluations, its computational cost remains constant regardless of $K$. In practice, we recommend using larger $K$ with smaller $\alpha$, which tends to yield more stable and interpretable steering outcomes.

Finally, LayerNavigator is roughly 20× faster than Top or Around Top 1, as it avoids repeated model inference. This efficiency opens up room for more fine-grained tuning over $K$ and $\alpha$ or even for fallback strategies when $K=1$ fails, making our method a better fit in realistic workflows.

Q7: Multiplier vs. Magnitude in Steering Strength

We greatly appreciate your comment regarding steering strength and would like to share our reflections.

The use of a scalar multiplier ($\alpha$) is indeed common in prior activation steering work—including our own—because the norms of steering vectors vary dramatically across layers. For instance, in the Conscientiousness task using MD extraction, the norm of the steering vector grows from 0.0045 at layer 1 to 1.5560 at layer 16, and 6.0952 at layer 32. This variation makes global control via a fixed magnitude difficult, whereas a multiplier relative to each vector’s native scale is more robust.

That said, we agree that fixed-magnitude scaling may encode a different inductive bias, especially in multi-layer steering, where allocating different absolute strengths across layers could potentially enhance alignment. We believe this is an exciting direction and plan to explore whether LayerNavigator’s steerability score can inform layer-specific magnitudes in a principled way.