A key challenge for Large Language Models (LLMs) is improving their Multilingual instruction-following ability over time without deteriorating their ability in languages they already excel at, typically English. In this paper, we study a two-phase Continual Fine-tuning (CFT) setup toward improving a model’s Multilingual adaptability. Concretely, we consider a two-phase CFT process in which an English-only end-to-end instruction fine-tuned LLM (Phase 1) is sequentially fine-tuned on a multilingual instruction dataset (Phase 2). Across MISTRAL-7B and LLAMA-3-8B and multiple dataset pairs, we show that instructional similarity between phases is critical: aligned datasets preserve or improve English while boosting multilingual ability, whereas misaligned datasets cause English degradation. We show that this degradation arises from representation shift during CFT, and that targeted mitigation strategies, including generative replay and heuristic-based layer freezing, reduce this shift and improve multilingual adaptation.