During language acquisition, children must learn when to generalize a pattern – applying it broadly and to new words (‘add –ed’ in English) – and when to restrict generalization, storing the pattern only with specific lexical items. But what governs when children will form productive rules during language acquisition? How do they determine when a pattern is widespread enough to generalize to novel words, and when a pattern should not extend beyond the cases they have observed in their input? One effort to quantify the conditions for generalization, the Tolerance Principle (Yang, 2016), has been shown to accurately predict children’s generalization behavior in dozens of corpus-based studies. The Tolerance Principle hypothesizes that a general rule will be formed when it is computationally more efficient than storing lexical forms individually. Here we test the Tolerance Principle in two artificial language experiments with children. In both experiments, we exposed children to a language with 9 novel nouns, some of which followed a regular pattern to form the plural (-ka) and some of which were exceptions to this rule. As predicted by the Tolerance Principle, in Experiment 1 we found that children exposed to 5 regular forms and 4 exceptions generalized, applying the regular form to 100% of novel test words. Children exposed to 3 regular forms and 6 exceptions did not extend the rule, even though the regular form was still the majority token in this condition. In Experiment 2, we found that children continued to behave categorically: either forming a productive rule (applying the regular form on all test trials) or using the regular form no more than predicted by chance. We found that the Tolerance Principle can be used to predict whether children will form a productive generalization or not based on each child’s individual vocabulary size. The Tolerance Principle appears to capture something fundamental about the way in which children form productive generalizations during language acquisition.