Instruction tuning (IT) has been a fruitful technique for aligning Large Language Models with human preferences. However, the linguistic implications of IT remain unclear. In two experiments on coreference and coherence biases in the context of Implicit Causality, we investigate how IT modulates these discourse biases in relation to model size. Our results show that IT interacts with model size -- instruction-tuned models display enhanced coherence biases and more human-like coreference patterns, sometimes exceeding human performance. However, this effect appears size-dependent, suggesting that IT causes some linguistic patterns to emerge that are dormant in the respective foundation models.