Human language acquisition involves diverse learning resources, including self-supervised learning (sequence prediction) and communicative interactions (talking to caregivers). While recent advancements in language models highlight the power of self-supervised learning, the role of communicative interaction remains unclear. This study uses Reinforcement Learning (RL) and parent-child agent simulations to model interactions and investigate their role in language acquisition, as well as whether RL-like mechanisms may function in children. We pretrained a small transformer model as a child agent, which then interacted with Google’s Gemini, acting as a parent agent, to learn language with the goal of being understood. Model evaluations show that the interactive training enhances intelligibility of model’s communication and increases behavioral similarity to real child speech. However, minimal pertaining alone provides noticeable syntactic and semantic competence, with RL yielding no consistent gains. These findings imply that interaction may play a more critical role in pragmatic aspects of language learning than in the development of linguistic structures, and that learning through interaction is a mechanism used by children.