[2506.08902] Intention-Conditioned Flow Occupancy Models

Computer Science > Machine Learning arXiv:2506.08902 (cs) [Submitted on 10 Jun 2025 (v1), last revised 28 Feb 2026 (this version, v3)] Title:Intention-Conditioned Flow Occupancy Models Authors:Chongyi Zheng, Seohong Park, Sergey Levine, Benjamin Eysenbach View a PDF of the paper titled Intention-Conditioned Flow Occupancy Models, by Chongyi Zheng and 3 other authors View PDF HTML (experimental) Abstract:Large-scale pre-training has fundamentally changed how machine learning research is done today: large foundation models are trained once, and then can be used by anyone in the community (including those without data or compute resources to train a model from scratch) to adapt and fine-tune to specific tasks. Applying this same framework to reinforcement learning (RL) is appealing because it offers compelling avenues for addressing core challenges in RL, including sample efficiency and robustness. However, there remains a fundamental challenge to pre-train large models in the context of RL: actions have long-term dependencies, so training a foundation model that reasons across time is important. Recent advances in generative AI have provided new tools for modeling highly complex distributions. In this paper, we build a probabilistic model to predict which states an agent will visit in the temporally distant future (i.e., an occupancy measure) using flow matching. As large datasets are often constructed by many distinct users performing distinct tasks, we include in our model...