Abstract: A model comprising a neural network is trained using a reproductive training architecture. A reward value and a change in reward (CIR) value are determined for each episode of training the model. If the reward value is less than a target value and if the CIR value is greater than a reproduction threshold value, a child model and corresponding branch is generated. If not, a pruning counter associated with the branch is incremented. The respective models of the branches undergo respective episodes of training. Branches that have a pruning counter greater than a pruning threshold are removed from further training. The pruning threshold may be dynamically determined. During successive episodes, poor branches are pruned, and satisfactory branches are retained. Pruning results in a substantial decrease in the number of unproductive episodes of training, reducing expenditure of computational resources while still resulting in one or more trained models with suitable performance.