Simple o3: Towards Interleaved Vision-Language Reasoning

• URL: http://arxiv.org/abs/2508.12109v1
• Date: Sat, 16 Aug 2025 17:15:39 GMT
• Title: Simple o3: Towards Interleaved Vision-Language Reasoning
• Abstract summary: We propose Simple o3, an end-to-end framework that integrates dynamic tool interactions into interleaved vision-language reasoning.<n>Our approach features a scalable data synthesis pipeline that generates high-quality interleaved vision-language reasoning chains.<n> Experimental results demonstrate Simple o3's superior performance on diverse benchmarks, outperforming existing approaches.
• Score: 38.46230601239066
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal Large Language Models (MLLMs) have shown impressive performance on vision-language tasks, but their long Chain-of-Thought (CoT) capabilities in multimodal scenarios remain underexplored. Inspired by OpenAI's o3 model, which emulates human-like ''thinking with image'' through iterative visual transformations and linguistic reasoning, we propose Simple o3, an end-to-end framework that integrates dynamic tool interactions (e.g., cropping, zooming, and reusing) into interleaved vision-language reasoning via supervised fine-tuning (SFT). Our approach features a scalable data synthesis pipeline that generates high-quality interleaved vision-language reasoning chains via an ''observe-reason-act'' cycle, complete with executable visual operations and rigorous verification, yielding the open-source TWI-Tools-146K dataset. Experimental results demonstrate Simple o3's superior performance on diverse benchmarks, outperforming existing approaches. By combining enhanced reasoning capabilities, Simple o3 establishes a powerful yet computationally affordable paradigm for advancing multimodal reasoning. Remarkably, we provide the first in-depth analysis of different interleaved reasoning strategies, offering insights into their impact on model performance. We found that by introducing additional visual tokens for interleaved vision-language reasoning, reusing and magnifying the original image significantly improves the model's visual reasoning and fine-grained perception, while image cropping based on precise visual grounding allows the model to effectively focus on key entities or regions, further enhancing its capabilities.

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