The margin-based approach proposed in the study can be extended to other types of human preference data beyond language models by adapting the concept of margin values to suit the specific characteristics of visual or multimodal tasks. In visual tasks, such as image recognition or object detection, the margin could represent the degree of difference in preference for one image over another. This could be quantified based on factors like visual similarity, relevance to a given context, or aesthetic appeal. For multimodal tasks that involve both text and images, the margin could capture the discrepancy in preference between a text description and an accompanying image. By incorporating margin values into the training process for reward models in these domains, the models can learn to prioritize responses or outputs that align more closely with human preferences, enhancing their performance and alignment with user expectations.