While emergent communication in artificial agents has been widely studied, interactions between previously separated populations remain underexplored, despite their real-world relevance. Our aim is to build a model of two pre-learned populations that meet and attempt to communicate. We develop an agent-based language evolution model, where agents are designed to resemble human internal development as closely as possible. These agents participate in ’language games’—atomic, scripted communication scenarios. When merging two pre-learned populations, we observe a significantly higher rate of successful communication compared to training all agents together from the beginning. This effect persists even after extended simulation of the merged population. Our findings suggest that merging pre-learned populations can enhance communication efficiency, offering practical insights for designing collaborative AI systems.