arXiv stat.ML
· Papers
A Theoretical Framework for Modular Learning of Robust Generative Models
arXiv:2602.17554v3 Announce Type: replace-cross Abstract: Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions: Can we train Large Language Models (LLMs) modularly, combining small, domain-specific experts to match monolithi