[LG] Intention-Conditioned Flow Occupancy Models C Zheng, S Park, S Levine, B Eysenbach [Princeton University & UC Berkeley] 本文提出的Intention-Conditioned Flow Occupancy Models (InFOM)通过创新性地结合潜在意图推断与基于流匹配的未来状态占有率建模，并在预训练中优化ELBO、在微调中使用隐式广义策略改进，成功地从未标记的异构离线数据中学习到了能够显著提升下游任务性能的RL基础模型，特别是在处理用户意图多样性和长期时间依赖性方面展现了巨大潜力。https://arxiv.org/abs/2506.08902
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