According to @godofprompt on Twitter, the RTL technique differentiates itself by learning multiple masks from the same initialization rather than applying global pruning once. Each mask is tailored to a specific data subset such as a class, cluster, or environment, and joint retraining is performed to refine these masks without interference. This approach, as shared by @godofprompt, provides new opportunities for neural network specialization and efficient model training.