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 continuous growth without human intervention or massive datasets.