Emergent communication models support interaction-based language learning, benefiting both Natural Language Processing (NLP) applications and simulations of language evolution, but they are prone to destabilizing language drift. Inspired by developmental trajectories in human language acquisition, this paper investigates whether age-based plasticity, where younger agents learn quickly and older agents maintain stable representations, can reduce language drift. In our set-up, static populations first reliably develop shared languages, followed by a phase in which population turnover gradually replaces older agents with new learners. Age-based plasticity significantly reduces drift in this setting, maintaining high accuracy and language similarity. In contrast, in populations with uniformly low plasticity agents cannot adapt quickly enough to integrate newcomers and in those with uniformly high plasticity the language changes faster than stable conventions can form. These findings demonstrate that developmental trajectories in individual learners substantially reduce overall language drift in dynamic populations.