The field of preference-based optimization is moving towards incorporating additional sources of information to improve the efficiency and effectiveness of optimization processes. Researchers are exploring ways to integrate sensor measurements, production costs, and perceptual ambiguity into preference-learning loops, leading to faster convergence and superior final solutions. This shift is enabling the development of more sophisticated optimization methods that can handle complex real-world problems. Notable papers in this area include: Regularized GLISp for sensor-guided human-in-the-loop optimization, which introduces a sensor-guided regularized extension of GLISp that integrates measurable descriptors into the preference-learning loop. Consecutive Preferential Bayesian Optimization, which generalizes preference-based optimization to explicitly account for production and evaluation costs. Feedback Descent, which optimizes text artifacts through structured textual feedback, rather than relying solely on scalar rewards. Preference is More Than Comparisons, which explores an alternative perspective based on feedback augmentation and introduces critical improvements to the model-free DB framework.