In Millimeter-wave extremely large-scale multiple-input multiple-output (XL-MIMO) communications, fast and reliable alignment of transceiver beams is essential for optimizing beamforming gain during data transmission. As XL-MIMO systems feature increasingly large antenna arrays, the electromagnetic propagation field shifts from the far field to the near field. In addition to angular domain beam training, near-field scenarios require distance domain beam training. However, many existing near-field beam training methods are essentially adaptations of far-field techniques, leading to issues such as excessive training overhead and suboptimal alignment. This paper introduces a position-aware beam training algorithm. In the initialization phase, position information is leveraged to reduce the beam search space. Subsequently, a multi-round iterative measurement strategy is employed. This approach updates the polar-domain codebook's posterior distribution using Bayes' rule based on measurements and dynamically adjusts the candidate beam set until the iteration termination condition is met. Numerical evaluations confirm the superiority of our proposed position-aware beam training algorithm, demonstrating significant enhancements in both normalized beamforming gain and achievable spectrum efficiency.