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The paper introduces Seq2Seq2Seq, a novel lossless compression method using a T5 language model architecture trained with reinforcement learning to compress data into discrete token sequences. This approach preserves the token-based structure of the original data, unlike autoencoders that use continuous latent spaces, leading to improved compression ratios. The model is trained using an off-policy reinforcement learning algorithm to optimize sequence length for minimal redundancy.
Ditch the black box: this new lossless compression method uses RL to train a Transformer to compress data into *discrete* token sequences, boosting compression ratios while preserving semantic structure.
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.