DeepEyes: 通过强化学习激励'图像思考'
DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learning
AI总结：
DeepEyes通过强化学习实现图像引导的推理，无需预训练数据，提升多模态任务性能。
摘要：
大型视觉-语言模型在多模态理解方面表现出色，但难以将视觉信息深度整合到其以文本为主的推理过程中，这是在模仿人类认知方面的一个关键挑战。为此，我们引入了DeepEyes模型，该模型学习
英文摘要：
Large Vision-Language Models excel at multimodal understanding but struggle to deeply integrate visual information into their predominantly text-based reasoning processes, a key challenge in mirroring human cognition. To address this, we introduce DeepEyes, a model that learns to "think with images", trained end-to-end with reinforcement learning without requiring pre-collected reasoning data for cold-start supervised fine-tuning (SFT). Notably, this ability emerges natively, leveraging the model's own grounding capability as an intrinsic function rather than relying on external specialized models or APIs. We enable this capability through active perception, where the model learns to strategically ground its reasoning in visual information, guided by a tailored data selection and reward strategy. DeepEyes achieves significant performance gains on general perception and reasoning benchmarks and also demonstrates improvement in grounding, hallucination, and mathematical reasoning tasks. Interestingly, we observe the distinct evolution of active perception from initial exploration to efficient and accurate exploitation, and diverse thinking patterns that closely mirror human visual reasoning processes. Code is available at https://github.com/Visual-Agent/DeepEyes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
