<cite>Tu and Gimpel (2018)</cite> developed an efficient framework for energy-based models by training "inference networks" to approximate structured inference instead of using gradient descent. However, <cite>their alternating optimization approach</cite> suffers from instabilities during training, requiring additional loss terms and careful hyperparameter tuning.