Aligning large language models (LLMs) with human preferences is crucial for their safe and effective deployment. Techniques like Reinforcement Learning from Human Feedback (RLHF) and Best-of-N (BoN) sampling have made strides in this area, but they often come with a hefty computational cost. BoN, in particular, involves generating multiple outputs and selecting the best, which can be computationally expensive, especially when repeated iteratively for better alignment. To address this, researchers have explored distillation methods like Best-of-N Distillation (BOND), training a smaller model to mimic the behavior of a larger, more computationally expensive BoN model. However, even these distillation approaches face efficiency challenges. The research paper "Faster WIND: Accelerating Iterative Best-of-N Distillation for LLM Alignment" introduces a novel approach to tackle these efficiency bottlenecks. The researchers establish a game-theoretic connection between iterative BoN and self-play alignment, offering a new perspective on how these seemingly different alignment methods are related. This insight leads to the development of WIN rate Dominance (WIND), a framework and accompanying algorithms for more efficient alignment. WIND focuses on optimizing the *win rate* of the LLM against a reference model, aiming to train a policy that consistently produces outputs preferred by humans. By focusing on win rate and using a clever optimization strategy, WIND achieves both faster computation and better sample efficiency compared to existing methods like BOND. Experimental results demonstrate that WIND not only accelerates the training process but also delivers superior performance across various benchmarks, particularly in reducing the number of samples needed and the overall training time. This makes iterative BoN-style distillation practical for larger models and complex alignment tasks, offering a promising pathway toward more efficient and effective LLM alignment.