标题： 通过强化学习解锁顿悟时刻：推进协作视觉理解和生成 显示英文标题

标题： Unlocking Aha Moments via Reinforcement Learning: Advancing Collaborative Visual Comprehension and Generation

摘要： 多模态大型语言模型（MLLMs）的近期研究旨在统一视觉理解和生成能力。然而，这两种能力仍然很大程度上彼此独立，仿佛它们是同一模型中的两个分离功能。因此，视觉理解并未促进视觉生成，而LLMs的推理机制也尚未被完全整合以彻底革新图像生成。本文提出使视觉理解和生成实现协作共进化，推动图像生成进入一个迭代反思的过程。我们引入了一种两阶段训练方法：监督微调通过教导MLLM具备生成真实CoT的基础能力来实现视觉生成，而强化学习则通过探索-利用权衡激活其全部潜力。最终，我们在视觉生成中解锁了“顿悟时刻”，使MLLMs从文本到图像任务扩展到统一的图像生成。广泛的实验表明，我们的模型不仅在文本到图像生成和图像编辑方面表现出色，还作为具有增强视觉理解能力的优越图像语义评估器发挥作用。项目页面：https://janus-pro-r1.github.io。 显示英文摘要

摘要： Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation. However, these two capabilities remain largely independent, as if they are two separate functions encapsulated within the same model. Consequently, visual comprehension does not enhance visual generation, and the reasoning mechanisms of LLMs have not been fully integrated to revolutionize image generation. In this paper, we propose to enable the collaborative co-evolution of visual comprehension and generation, advancing image generation into an iterative introspective process. We introduce a two-stage training approach: supervised fine-tuning teaches the MLLM with the foundational ability to generate genuine CoT for visual generation, while reinforcement learning activates its full potential via an exploration-exploitation trade-off. Ultimately, we unlock the Aha moment in visual generation, advancing MLLMs from text-to-image tasks to unified image generation. Extensive experiments demonstrate that our model not only excels in text-to-image generation and image editing, but also functions as a superior image semantic evaluator with enhanced visual comprehension capabilities. Project Page: https://janus-pro-r1.github.io.