Abstract: Sequence models have demonstrated remarkable success in behavioral planning by leveraging previously collected demonstrations. However, generating consistent long-horizon plans remains a significant challenge, particularly when the planner must adapt to unseen constraints and environments, such as discovering goals, and navigate to them, even if it involves interacting with obstacles along the way. Such behavioral planning problems are difficult to solve due to agents failing to adapt beyond the single task learned through their reward function and the inability to generalize to new environments not covered in the training demonstrations, e.g., environments with no walls in the demonstrations. Consequently, state-of-the-art decision-making methods are limited to missions where the required tasks are well-represented in the training demonstrations and can be solved within a short (temporal) planning horizon. To address this, we propose GenPlan: a stochastic and adaptive planner that leverages discrete-flow models for generative sequence modeling, enabling sample-efficient exploration and exploitation. This framework relies on an iterative denoising procedure to generate a sequence of goals and actions. This approach captures multi-modal action distributions and facilitates goal and task discovery, thereby enhancing generalization to out-of-distribution tasks and environments, i.e., missions not part of the training data. We demonstrate the effectiveness of our method through multiple simulation environments. Notably, GenPlan outperforms the state-of-the-art methods by over 10% on adaptive planning tasks, where the agent adapts to multi-task missions while leveraging demonstrations on single-goal-reaching tasks.