Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges

Decision-making is a dynamic process requiring Self-supervised pretraining has enabled large sequence perception, memory, and reasoning to make models to realize few-shot or even zero-shot adaptation in choices and find optimal policies. Traditional natural language processing (NLP) [OpenAI, 2023] and computer approaches to decision-making suffer from sample vision (CV) tasks [Bai et al., 2023]. Through pretraining efficiency and generalization, while largescale on large generic corpora or visual data (images and self-supervised pretraining has enabled fast videos), knowledge about the world and human society is adaptation with fine-tuning or few-shot learning learned which can be utilized in various downstream task in language and vision. We thus argue to integrate learning with few samples so as to improve sample efficiency knowledge acquired from generic largescale and generalization.