Large language models are often compared to human learners based on the amount of training data required or the end state capabilities of a learner, yet less attention has been given to differences in their language learning process. This study uses determiner acquisition as a case study to characterize how LLMs and children differ in their learning processes. By analyzing annotated speech samples from specified age ranges of four children and intermediate training checkpoints of the Pythia-70m language model, we trace the learners’ learning paths of definite and indefinite determiner use. Our results reveal a divergence: the children first produce the indefinite determiner, while the model first produces the definite determiner. This difference reflects underlying differences in the learning goals and mechanisms of models and children. Framing language learning as movement over distributions of linguistic features makes the learning process visible and offers an alternative approach for comparing humans and language models.