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In the fields of natural language processing (NLP) and computer vision (CV), recent advances in generative modeling have led to powerful machine learning systems that can effectively learn from large labeled and unlabeled datasets. These systems, by and large, apply a uniform pretrain-finetune pipeline on sequential data streams and have achieved state-of-the-art-performance across many tasks and benchmarks. In this talk, we will present recent algorithms that extend this paradigm to sequential decision making, by casting it as an inverse problem that can be solved via deep generative models. These generative approaches are stable to train, provide a flexible interface for single- and multi-task inference, and generalize exceedingly well outside their training datasets. We instantiate these algorithms in the context of reinforcement learning and black-box optimization. Empirically, we demonstrate that these approaches perform exceedingly well on high-dimensional benchmarks outperforming the current state-of-the-art approaches based on forward models.