This paper introduces **Personalized Alignment at Decoding-time (PAD)**, a framework designed to tailor Large Language Model (LLM) outputs to specific user preferences without the need for expensive retraining. Traditional alignment methods often rely on a "one-size-fits-all" approach, but **PAD** uses a unique **personalized reward model (PersRM)** to adjust token-level predictions during the inference phase. By **decoupling text generation from user values**, the system can adapt to diverse cultural, educational, or political leanings in real-time. Experimental results show that **PAD** excels at generalizing to **unseen preferences** and works effectively across various base models. Ultimately, the authors provide a **training-free solution** that balances high-quality personalization with computational efficiency.