Best of N

"Best-of-N" methods aim to improve the performance of large language models (LLMs) and other AI systems by selecting the best output from multiple generated candidates. Current research focuses on optimizing this process through adaptive sampling techniques that reduce computational cost while maintaining accuracy, exploring alternative training methods like distillation to mimic the benefits of Best-of-N without the high inference overhead, and analyzing the inherent biases and limitations of these approaches. These advancements are crucial for enhancing the efficiency and reliability of LLMs, particularly in applications requiring high-quality outputs and mitigating risks associated with AI decision-making.