Big Cooperative Learning

• URL: http://arxiv.org/abs/2407.21319v1
• Date: Wed, 31 Jul 2024 03:59:14 GMT
• Title: Big Cooperative Learning
• Authors: Yulai Cong,
• Abstract summary: We show that the training of foundation models can be interpreted as a form of big cooperative learning. We propose the BigLearn-GAN, which is a novel adversarially-trained foundation model with versatile data sampling capabilities.
• Score: 7.958840888809145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cooperation plays a pivotal role in the evolution of human intelligence; moreover, it also underlies the recent revolutionary advancement of artificial intelligence (AI) that is driven by foundation models. Specifically, we reveal that the training of foundation models can be interpreted as a form of big cooperative learning (\textit{abbr.} big learning), where massive learning individuals/tasks \emph{cooperate} to approach the unique essence of data from diverse perspectives of data prediction, leveraging a universal model. The presented big learning therefore unifies most training objectives of foundation models within a consistent framework, where their underlying assumptions are exposed simultaneously. We design tailored simulations to demonstrate the principle of big learning, based on which we provide learning-perspective justifications for the successes of foundation models, with interesting side-products. Furthermore, we reveal that big learning is a new dimension for upgrading conventional machine learning paradigms, valuable for endowing reinvigorations to associated applications; as an illustrative example, we propose the BigLearn-GAN, which is a novel adversarially-trained foundation model with versatile data sampling capabilities. Code is available at \texttt{https://github.com/YulaiCong/BigCooperativeLearning}.

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