The field of image editing is moving towards more efficient and effective methods, leveraging reinforcement learning (RL) and diffusion models to achieve high-fidelity results. Recent developments have focused on overcoming the limitations of current models, such as the lack of high-fidelity reward signals and the difficulty of editing images while preserving their semantic content. Notable advancements include the development of specialized reward models, novel inversion techniques, and training-free guidance methods. These innovations have enabled significant improvements in image editing tasks, including text-guided editing, noise map inversion, and low-resolution image generation. Noteworthy papers include: EditScore, which presents a comprehensive methodology for unlocking online RL for image editing via high-fidelity reward modeling. Semantic Editing with Coupled Stochastic Differential Equations, which proposes a simple yet powerful tool for controlled generative AI. Editable Noise Map Inversion, which introduces a novel inversion technique for high-fidelity image manipulation. Training-Free Reward-Guided Image Editing via Trajectory Optimal Control, which formulates the editing process as a trajectory optimal control problem. NoiseShift, which proposes a training-free method for resolution-aware noise recalibration.