Large language models (LLMs) have revolutionized how we interact with machines, but aligning them perfectly with human preferences remains a challenge. Current methods, like Reinforcement Learning with Human Feedback (RLHF), can be complex and resource-intensive. Offline RLHF offers a simpler alternative, but traditional approaches often miss the nuances of human feedback. They capture *what* we prefer but not *how much* we prefer it. Imagine asking an AI to write a poem. Two versions might be technically correct, but one resonates more deeply. Existing offline RLHF struggles to capture this distinction. This research introduces a novel approach called Reward Difference Optimization (RDO) to address this limitation. Instead of just noting which response is better, RDO quantifies the *degree* of preference. This is done by calculating 'reward difference coefficients'—essentially weights assigned to pairs of responses. Larger differences in preference receive higher weights, guiding the LLM to learn more from significant distinctions. This is analogous to a teacher emphasizing key concepts to students. RDO goes beyond simply measuring the difference in assigned scores. It introduces a ‘difference model’ specifically trained to predict the gap in human preference between two responses. By considering the interplay between responses, this model gains a richer understanding of human feedback. This is akin to understanding the 'why' behind a choice, not just the choice itself. Experiments with a 7B LLM on established datasets showed that RDO significantly improves performance. Not only did automatic metrics increase, but human evaluations confirmed that RDO-trained models produced responses that better aligned with human preferences. This has far-reaching implications for building more human-centric AI systems. The future of RDO looks bright. Researchers are exploring how these techniques scale to larger LLMs and how they affect an LLM’s ability to generalize to different tasks. While there’s still much to learn, RDO marks a significant step towards harnessing the full potential of human feedback in shaping the future of AI.