The field of multimodal image generation is moving towards more efficient and adaptive models. Researchers are exploring new frameworks that integrate reasoning, generation, and self-evaluation to enhance image fidelity and semantic alignment. Reinforcement learning is being used to endow AI systems with the capacity to autonomously decompose, reorder, and combine visual models. Additionally, there is a focus on developing methods that can select good examples for fine-tuning and can generate high-quality images with fewer samples. Noteworthy papers include ImAgent, which introduces a unified multimodal agent framework for efficient test-time scaling, and Image-POSER, which proposes a reflective reinforcement learning framework for multi-expert image generation and editing. UniGen-1.5 is also notable for its unified Reinforcement Learning strategy that improves both image generation and image editing jointly via shared reward models.