Does Fine-Tuning Undo Activation Steering? Behavioural Recovery Without Weight-Edit Reversal

A new arXiv preprint (arXiv:2608.24988v1) finds that activation steering embedded in language model weights survives fine-tuning mechanistically but not behaviorally: across five instruction-tuned models (3B-14B), refusal suppression lost 64% of its effect on average under supervised fine-tuning (SFT), yet the weight edit remained nearly intact (mean vector recovery ρ = 0.004, mean cosine similarity 0.074). The authors conclude that embedded steering is mechanistically durable but functionally vulnerable, requiring behavioral re-validation after downstream training.

arXiv:2608.24988v1 Announce Type: new Abstract: Activation steering can be embedded directly into a language model's weights, shaping behaviour without inference-time intervention and offering a way to encode alignment prior to release. However, models are routinely fine-tuned after deployment, and it is unknown whether embedded interventions survive this. We study the stability of embedded steering for refusal suppression and brevity induction across five instruction-tuned models 3B-14B under non-adversarial SFT and RLHF. Behaviourally, preservation tracks the training data: steering degrades when optimisation pressure contradicts the targeted behaviour and persists otherwise, with refusal ablation losing 64% of its effect on average under SFT. Mechanistically, however, the weight edit survives almost untouched even where behaviour reverts: mean vector recovery is $\rho = 0.004$, and the fine-tuning update along the steering direction is near-orthogonal to its pre-edit weight pattern mean $\cos\theta = 0.074$ . When steered behaviour degrades, fine-tuning does not achieve it by dismantling or reversing the steering mechanism itself. Embedded steering is therefore mechanistically durable but functionally vulnerable, and requires behavioural re-validation after downstream training.