RGM-SL: Receiver-Aware Gradient Matching for Socialized Generalist Cultivation

Socialized learning brings together domain experts to cultivate across-domain generalist and return its integrated knowledge to the expert community. Under pathological label partitions, this process must transfer complementary local knowledge as the generalist's learning needs evolve. We introduce RGM-SL, a receiver-aware gradient matching approach to socialized generalist cultivation. After an initial bootstrap stage, each expert uses the current generalist as the receiving model and optimizes a compact synthetic dataset to align real- and synthetic-data gradients under its training objective. This makes the transmitted data responsive to both local expert knowledge and the generalist's current state. The framework coordinates model-level receiver states, sample-level synthetic data, and label-level supervision. The server aggregates expert uploads and trains the generalist with hard labels, class-masked soft labels, and expert anchoring. The resulting shared representation supports lightweight feedback heads that extend the experts' cross-domain capabilities. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet show that the complete receiver-aware refinement procedure improves overall generalist accuracy over random real-sample selection at matched images-per-class and round budgets on all three datasets. Generalist feedback further improves community overall accuracy and enables every expert to recognize classes outside its original domain.