Text steganography is a widely used method for covert communication, which primarily relies on natural language processing (NLP) techniques to embed hidden information using linguistic features within text. However, existing generative text encoding methods often face significant information capacity loss during the rejection process. In this paper, we propose a novel framework for text encoding: the Reject-Split Steganography Framework (RS-Stega). This framework leverages existing models to perform quality control on generated text, minimizing the impact on embedding capacity. The key innovation of our approach is its ability to adaptively select tokens based on context using the rejection mechanism, ensuring both fluency and coherence in the cover text, while optimizing embedding capacity with the split mechanism. Extensive comparative and ablation studies on three benchmark datasets demonstrate the effectiveness of our proposed framework. The results show that RS-Stega significantly improves both information capacity and cover text quality, achieving a 52.22% reduction in perplexity (PPL) and a 49.63% increase in embedding capacity (BPW). These results underscore the effectiveness and robustness of our method across various datasets, highlighting its applicability in diverse real-world scenarios where both data concealment and text naturalness are essential. (Code and data are available at https://github.com/Poshang-Taoist/RS-Stega )