Previous research on sparse feature matching typically involves a staged optimization process of keypoint detection, description, and matching. While it allows the network to adapt to specific inputs, it may limit the network's expressive capability and the overall architectural flexibility. In this study, we rethink the matching framework and propose to directly match any given keypoints, optimizing the matching network in an approximately end-to-end manner. To achieve this, firstly, we dynamically sample random positions within the images as assumed keypoints during training, allowing the network to explore a broader matching space. Secondly, we replace specific descriptors with high-efficiency sparse embeddings at multi levels of the image, facilitating the direct learning of underlying textures. Thirdly, we propose a novel and promising architecture, called Proposal-Guided TRansformer (PGTR), which aggregates context information from neighboring match proposals instead of searching globally with local features. PGTR works especially well under our training approach, and attain a synergistic advantage in terms of performance and efficiency. The overall pipeline achieves outstanding performance on various keypoints without any retraining, and can be flexibly reused when new keypoints emerge, making it valuable for real-world applications. Code will be available.