Listen and Chant Before You Read: The Ladder of Beauty in LM Pre-Training

We show that pre-training a Transformer on music before language significantly accelerates language acquisition. Using piano performances (MAESTRO dataset), a developmental pipeline -- music → poetry → prose -- yields a 17.5% perplexity improvement over random initialization (p < 0.001, 5 seeds), with music and poetry improving orthogonal model components (internal computation and embeddings, respectively). Convergence tests confirm that this is not a transient head start: at d!=!64, multi-seed validation (5 seeds) shows a persistent 5.5% gap at plateau (p = 0.017), with the pipeline converging faster and to a lower loss in every run. Real music matches the transfer ceiling of synthetic patterns with one-third the data, and scaling experiments reveal that optimal pre-training data volume shifts with model capacity (-3% → +3% → +6% advantage of larger datasets from d!=!16 to d!=!64). Across the scales we study (d!∈!16,32,64, up to ~400K parameters), these results suggest a capacity-dependent data curation principle and indicate that structured human creative outputs can provide an efficient pre-training substrate for small language models; stronger conclusions at modern pre-training scale will require substantially larger experiments.