A Theoretical Framework for Modular Learning of Robust Generative Models

arXiv:2602.17554v2 Announce Type: replace 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...

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