A new approach called SWARR enhances Sliding-Window Attention (SWA) for mathematical reasoning by combining supervised fine-tuning and reinforcement learning, addressing the limitations of traditional self-attention models. This method significantly improves SWA's performance while maintaining its efficiency, making it a competitive option for long-context inference tasks in large language models.