Efficient lossless compression is essential for minimizing storage costs and transmission overhead while preserving data integrity. Traditional compression techniques, such as dictionary-based and statistical methods, often struggle to optimally exploit the structure and redundancy in complex data formats. Recent advancements in deep learning have opened new avenues for compression; however, many existing approaches depend on dense vector representations that obscure the underlying token structure. To address these limitations, we propose a novel lossless compression method that leverages Reinforcement Learning applied to a T5 language model architecture. This approach enables the compression of data into sequences of tokens rather than traditional vector representations. Unlike auto-encoders, which typically encode information into continuous latent spaces, our method preserves the token-based structure, aligning more closely with the original data format. This preservation allows for higher compression ratios while maintaining semantic integrity. By training the model using an off-policy Reinforcement Learning algorithm, we optimize sequence length to minimize redundancy and enhance compression efficiency. Our method introduces an efficient and adaptive data compression system built upon advanced Reinforcement Learning techniques, functioning independently of external grammatical or world knowledge. This approach shows significant improvements in compression ratios compared to conventional methods. By leveraging the latent information within language models, our system effectively compresses data without requiring explicit content understanding, paving the way for more robust and practical compression solutions across various applications.

翻译：高效的无损压缩对于在保持数据完整性的同时最小化存储成本与传输开销至关重要。传统的压缩技术（如基于字典和统计的方法）通常难以充分利用复杂数据格式中的结构与冗余。深度学习的近期进展为压缩开辟了新途径；然而，现有方法多依赖于稠密向量表示，这模糊了底层的标记结构。为应对这些局限，我们提出了一种新颖的无损压缩方法，该方法将强化学习应用于T5语言模型架构。此方法能够将数据压缩为标记序列，而非传统的向量表示。与通常将信息编码至连续潜在空间的自编码器不同，我们的方法保留了基于标记的结构，从而更贴近原始数据格式。这种保留特性在保持语义完整性的同时实现了更高的压缩率。通过使用离策略强化学习算法训练模型，我们优化序列长度以最小化冗余并提升压缩效率。我们的方法基于先进的强化学习技术构建了一个高效且自适应的数据压缩系统，其运行独立于外部语法或世界知识。相较于传统方法，该方法在压缩率上展现出显著提升。通过利用语言模型中的潜在信息，我们的系统无需显式内容理解即可有效压缩数据，为跨多种应用实现更鲁棒且实用的压缩解决方案铺平了道路。