Reinforcement Learning from Human Feedback (RLHF) has revolutionized Large Language Models (LLMs), but it comes with challenges. Gathering human preference data is resource-intensive and complex. While synthetic data offers a solution, it often misaligns with true human preferences. Now, a groundbreaking approach called Generative Reward Models (GenRM) bridges the gap between human and AI feedback. GenRM iteratively trains LLMs on self-generated reasoning traces, producing synthetic preferences that better reflect human values. Unlike traditional reward models that struggle with unfamiliar tasks, GenRM excels in out-of-distribution scenarios, essential for deploying LLMs in real-world settings. A key innovation is the CoT-GenRM, which integrates chain-of-thought reasoning. This encourages the model to think step-by-step, improving its decision-making and generalization, particularly in complex reasoning and safety-related tasks. Experiments reveal that CoT-GenRM outperforms traditional reward models, especially in safety-critical areas. The implications are huge. GenRM's scalability reduces the need for extensive human input, making LLM alignment more practical. Its robustness allows it to adapt to new scenarios and real-world applications, offering significant potential for aligning AI systems with human values more efficiently and effectively. The future of GenRM is bright. Ongoing research is exploring iterative online optimization to enable real-time adaptation and multimodal feedback to address complex problems. GenRM is paving the way for more reliable and aligned AI systems.