[2603.01935] Dream2Learn: Structured Generative Dreaming for Continual Learning

Computer Science > Machine Learning arXiv:2603.01935 (cs) [Submitted on 2 Mar 2026] Title:Dream2Learn: Structured Generative Dreaming for Continual Learning Authors:Salvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri, Amelia Sorrenti, Simone Palazzo, Concetto Spampinato, Giovanni Bellitto View a PDF of the paper titled Dream2Learn: Structured Generative Dreaming for Continual Learning, by Salvatore Calcagno and 6 other authors View PDF HTML (experimental) Abstract:Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowledge restructuring, we introduce Dream2Learn (D2L), a framework in which a model autonomously generates structured synthetic experiences from its own internal representations and uses them for self-improvement. Rather than reconstructing past data as in generative replay, D2L enables a classifier to create novel, semantically distinct dreamed classes that are coherent with its learned knowledge yet do not correspond to previously observed data. These dreamed samples are produced by conditioning a frozen diffusion model through soft prompt optimization driven by the classifier itself. The generated data are not used to replace memory, but to expand and reorganize the representation space, effectively allowing the network to self-train on internally synthesized concepts. By integrating dreamed classes into continual training, ...