AdaRevD is a novel image deblurring method that pushes the limits of state-of-the-art (SOTA) deblurring networks by exploring their insufficient decoding capabilities. The method introduces an adaptive patch exiting reversible decoder (AdaRevD) that enhances the decoding capacity while maintaining low GPU memory usage. The decoder is composed of multiple sub-decoders, each trained in a reversible manner, allowing the model to progressively disentangle high-level degradation information from low-level blur patterns. A classifier is used to predict the degradation degree of image patches, enabling the model to exit at the appropriate sub-decoder for speedup. This approach allows AdaRevD to achieve a PSNR of 34.60 dB on the GoPro dataset, outperforming other SOTA methods. The method also demonstrates effective performance on other datasets, including HIDE and RealBlur. The reversible structure of AdaRevD enables the model to maintain low memory consumption while enhancing the decoding capacity, making it suitable for large-scale image deblurring tasks. The method's ability to adaptively exit at different sub-decoders based on the degradation degree of the image patches contributes to its superior performance in image deblurring.AdaRevD is a novel image deblurring method that pushes the limits of state-of-the-art (SOTA) deblurring networks by exploring their insufficient decoding capabilities. The method introduces an adaptive patch exiting reversible decoder (AdaRevD) that enhances the decoding capacity while maintaining low GPU memory usage. The decoder is composed of multiple sub-decoders, each trained in a reversible manner, allowing the model to progressively disentangle high-level degradation information from low-level blur patterns. A classifier is used to predict the degradation degree of image patches, enabling the model to exit at the appropriate sub-decoder for speedup. This approach allows AdaRevD to achieve a PSNR of 34.60 dB on the GoPro dataset, outperforming other SOTA methods. The method also demonstrates effective performance on other datasets, including HIDE and RealBlur. The reversible structure of AdaRevD enables the model to maintain low memory consumption while enhancing the decoding capacity, making it suitable for large-scale image deblurring tasks. The method's ability to adaptively exit at different sub-decoders based on the degradation degree of the image patches contributes to its superior performance in image deblurring.