KDoS introduces knowledge density to guide synthetic data generation through a three-stage feedback mechanism. Experiments on models from 0.6B to 16B and data scales from 1B to 5B tokens show that an optimal knowledge distribution consistently maximizes knowledge boundary expansion, is stable across model backbones, and outperforms baselines on six knowledge benchmarks.