The automatic generation of counter-speech (CS) is a critical strategy for addressing hate speech by providing constructive and informed responses. However, existing methods often fail to generate high-quality, impactful, and scalable CS, particularly across diverse lin- guistic contexts. In this paper, we propose a novel methodology to enhance CS generation by aligning Large Language Models (LLMs) using Supervised Fine-Tuning (SFT) and Di- rect Preference Optimization (DPO). Our ap- proach leverages DPO to align LLM outputs with human preferences, ensuring contextu- ally appropriate and linguistically adaptable responses. Additionally, we incorporate knowl- edge grounding to enhance the factual accuracy and relevance of generated CS. Experimental results demonstrate that DPO-aligned models significantly outperform SFT baselines on CS benchmarks while scaling effectively to mul- tiple languages. These findings highlight the potential of preference-based alignment tech- niques to advance CS generation across var- ied linguistic settings. The model supervision and alignment is done in English and the same model is used for reporting metrics across other languages like Basque, Italian, and Spanish.