Aligning large language models (LLMs) with human preferences is crucial as AI becomes increasingly integrated into our lives. Traditionally, complex methods like Direct Preference Optimization (DPO) have been used, requiring extensive hyperparameter tuning to achieve optimal performance. But what if simpler, more robust methods could achieve even better results? New research explores this question by comparing DPO with two alternative methods: Length-Normalized DPO (LN-DPO) and Simple Preference Optimization (SimPO). The study reveals surprising findings, especially regarding the robustness of these methods to varying hyperparameters in real-world scenarios, like ensuring AI safety and helpfulness. The results suggest that SimPO and LN-DPO might offer more stable and efficient alignment solutions compared to the widely-used DPO. SimPO, in particular, stands out due to its simplicity and ability to produce concise, high-quality responses. Moreover, it requires less training time than DPO, making it a more practical choice for broader application. The research delves into the nuances of these methods by analyzing key metrics such as response length, KL divergence, and win rates against chosen and SFT responses. The findings highlight not only the strengths of SimPO and LN-DPO but also the potential pitfalls of relying solely on DPO for AI alignment. The implications are significant for the future of AI development, as more robust and efficient alignment methods will be crucial for deploying safer and more reliable LLMs in real-world applications. This research offers valuable insights for practitioners, including guidance on hyperparameter tuning and selecting the most suitable alignment method for specific needs. The study opens up exciting new avenues for exploring simpler yet more effective approaches to AI alignment, paving the way for a future where humans and AI can collaborate more seamlessly and safely.