Extracting finite state automata (FSAs) from black-box models offers a powerful approach to gaining interpretable insights into complex model behaviors.To support this pursuit, we present a weighted variant of Angluin's (1987) L ˚algorithm for learning FSAs.We stay faithful to the original algorithm, devising a way to exactly learn deterministic weighted FSAs whose weights support division.Furthermore, we formulate the learning process in a manner that highlights the connection with FSA minimization, showing how L ˚directly learns a minimal automaton for the target language.github.com/rycolab/weighted-angluin