A new method for selecting synthetic data to fine-tune large language models (LLMs) is proposed, focusing on maximizing the value of synthetic data to the target task. The method, called Training-Aware Target Coverage (TATC), is based on a linear theory that characterizes the tradeoff between the benefits and errors of synthetic data. Experimental results show that TATC outperforms alternative methods on various tasks.