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[2602.17554] A Theoretical Framework for Modular Learning of Robust Generative Models
Summary
This article presents a theoretical framework for modular learning in robust generative models, exploring the combination of domain-specific experts to enhance performance without extensive heuristic tuning.
Why It Matters
As generative models become increasingly resource-intensive, this framework offers a promising solution to improve efficiency and robustness in training. By leveraging modularity, it addresses key challenges in generative modeling, potentially leading to significant advancements in AI applications.
Key Takeaways
- Modular learning can combine small, domain-specific models to achieve performance comparable to larger monolithic models.
- The framework introduces a robust gating mechanism that minimizes divergence across data mixtures, enhancing model reliability.
- Empirical results demonstrate that modular architectures can outperform traditional models, mitigating issues like gradient conflict.
Computer Science > Machine Learning arXiv:2602.17554 (cs) [Submitted on 19 Feb 2026] Title:A Theoretical Framework for Modular Learning of Robust Generative Models Authors:Corinna Cortes, Mehryar Mohri, Yutao Zhong View a PDF of the paper titled A Theoretical Framework for Modular Learning of Robust Generative Models, by Corinna Cortes and 2 other authors View PDF 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 monolithic performance-and can we do so robustly for any data mixture, eliminating heuristic tuning? We present a theoretical framework for modular generative modeling where a set of pre-trained experts are combined via a gating mechanism. We define the space of normalized gating functions, $G_{1}$, and formulate the problem as a minimax game to find a single robust gate that minimizes divergence to the worst-case data mixture. We prove the existence of such a robust gate using Kakutani's fixed-point theorem and show that modularity acts as a strong regularizer, with generalization bounds scaling with the lightweight gate's complexity. Furthermore, we prove that this modular approach can theoretically outperform models retrained on aggregate data, with the gap characterized by the Jensen-Shannon Divergence. Finally, we introduce a scalable Stochastic Primal...
