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Enhancing Multimodal AI with Token Discrepancy Loss
This chapter examines token discrepancy loss as a method to integrate visual and textual information in AI models, enhancing their performance in multimodal tasks. It discusses the challenges and insights gained from training models that optimize reasoning through accurate visualizations. The chapter highlights the fine-tuning process, current model limitations, and the potential for future advancements in multimodal reasoning.