Chain-of-Thought (CoT) prompting has proven effective for enhancing the reasoning abilities of Large Language Models (LLMs), but it comes at a cost. The verbosity associated with CoT prompts leads to increased computational demands. Many existing studies tend to focus solely on the output quality, neglecting the intermediate reasoning steps, which makes it difficult to analyze how models arrive at their conclusions. To tackle these issues, the authors introduced Render-of-Thought (RoT), a novel framework that transforms textual reasoning steps into images. This approach clarifies the reasoning process, allowing better traceability of the model's logic. The method uses existing Vision Language Models (VLMs) to align visual and textual representations effectively. This integration enables RoT to be implemented without requiring additional pre-training, making it user-friendly for developers. Experimental results on various mathematical and logical reasoning tasks show that RoT can compress token usage by 3 to 4 times compared to traditional CoT methods. It also significantly speeds up inference times while maintaining competitive performance with existing techniques. The authors have made their code publicly available, encouraging further exploration and development in this area.