Schema linking is a crucial step in Text-to-SQL systems, connecting natural language mentions to relevant database tables and columns. Despite recent advances in LLM-based Text-to-SQL models, current approaches face two fundamental limitations: (1) providing detailed column descriptions inflates the input context, often exceeding the model’s effective sequence length, and (2) predicting multi-token column names increases output complexity, making the task fragile to generation errors. To address these challenges, we propose SchemaToken, which represents each database column as a dedicated token in the LLM’s vocabulary. This design compresses column semantics into single embeddings, reducing context length and simplifying prediction. We introduce a two-stage training paradigm: (i) Semantic Alignment, which enables the LLM to understand the newly added schema tokens via structured embeddings, and (ii) Pseudo-task Alignment, which trains the LLM to generate these tokens as an unordered set using reinforcement learning with a Jaccard-based reward. Experiments on multiple benchmark datasets demonstrate that SchemaToken significantly improves schema linking performance, particularly for large and complex schemas, while maintaining computational efficiency. Our code is publicly available at https://github.com/MrBlankness/SchemaToken.