What statistical properties might support learning abstract grammatical knowledge from linear input?We address this question by examining the statistical distribution of function words.Function words have been argued to aid acquisition through three distributional properties: high frequency, reliable syntactic association, and phrase-boundary alignment.We conduct a cross-linguistic corpus analysis of 186 languages, which confirms that all three properties are universal.Using counterfactual language modeling and ablation experiments on English, we show that preserving these properties facilitates acquisition in neural learners, with a Goldilocks effect: function words must be frequent enough to be reliable, yet diverse enough to remain informative to structural dependency.Probing analyses further reveal that different learning conditions produce systematically different reliance on function words. 1