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

• URL: http://arxiv.org/abs/2506.01480v1
• Date: Mon, 02 Jun 2025 09:39:28 GMT
• Title: Unlocking Aha Moments via Reinforcement Learning: Advancing Collaborative Visual Comprehension and Generation
• Authors: Kaihang Pan, Yang Wu, Wendong Bu, Kai Shen, Juncheng Li, Yingting Wang, Yunfei Li, Siliang Tang, Jun Xiao, Fei Wu, Hang Zhao, Yueting Zhuang,
• Abstract summary: We propose to enable the collaborative co-evolution of visual comprehension and generation.<n>We introduce a two-stage training approach: supervised fine-tuning teaches the MLLM with the foundational ability to generate genuine CoT.<n>We unlock the Aha moment in visual generation, advancing MLLMs from text-to-image tasks to unified image generation.
• Score: 85.22602924467603
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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