B-STAR is a self-improvement framework that helps AI models learn by balancing exploration and exploitation. It dynamically adjusts parameters like sampling temperature and reward thresholds to maintain a steady flow of high-quality training data, boosting performance in tasks such as math, coding, and logic. This adaptive method surpasses older approaches like STaR and RFT, offering