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

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 ablation and brevity amplification across five instruction-tuned models (3B-14B) under non-adversarial SFT and RLHF. We find that steering degrades more under stronger optimisation pressure against the steered behaviour, which depends on both the training data and the training algorithm. Refusal ablation loses 69% of its effect on average under our SFT setup, but only 2% under our RLHF setup. The weight-edit, by contrast, remains almost unchanged (on average only 0.4% of it is restored), and the fine-tuning update along the steering direction is close to orthogonal to the pre-edit weight pattern (mean cosine similarity 0.071). Fine-tuning that restores the behaviour does so without reversing the edit. As the steered behaviour is not fully durable, embedded steering should be re-validated after downstream training.

arXiv commentAccepted at EMNLP 2026 Main. Earlier version at ICLR 2026 Re^4-Align Workshop