Large language models need several orders of magnitude more text than a child hears to reach a comparable level of linguistic skill. One reason is that a particularly useful capability, the induction head (a circuit that completes a pattern of the form [A] [B] ... [A] by copying [B]), is not built in but must emerge during training, and does so only after the model has read a great deal of text. We ask whether supplying this capability directly, as a fixed part of the architecture, improves learning when data is scarce. We present a three-layer, attention-free language model of about 12M parameters in which all long-range mixing is performed by a non-learned, sub-quadratic induction operator (its cost grows more slowly than the square of the sequence length): for each position it finds recent earlier occurrences of the same token and copies the continuation of the best match. Trained on the 10M-word BabyLM Strict-Small corpus, the model learns markedly faster than a parameter-matched self-attention baseline and stays competitive at convergence; on the public BabyLM evaluation it is competitive with the challenge’s GPT-2 baselines on grammatical minimal pairs with a tenth of the parameters. Targeted interventions show the operator is used as intended: predictions travel along its links, the links are necessary, and stacking layers composes them into two-step retrieval. These results motivate hybrid designs that interleave exact look-back with attention.