JointLoom: Compact-State GPU Simulation and Streaming Imitation Learning for Multi-Agent Pathfinding

Multi-agent path finding (MAPF) requires multiple agents to reach their goals without conflicts. While search-based methods have long dominated MAPF, learned models are increasingly used to predict actions or guide search. Their execution and training require evolving joint states to continuously supply structured model inputs, creating a new computational bottleneck between MAPF state evolution and neural computation. To address this challenge, we propose , a compact-state GPU reconstruction framework for efficient learning-based MAPF. 1) introduces a reconstructible-state contract that preserves complete joint configurations and consistent input semantics, 2) performs on-demand GPU reconstruction with dependency-aware reuse to reduce redundant input construction and transfer, and 3) asynchronously streams compact states to reduce learner waiting during expert-supervised training. Extensive experiments with graph-based MAGAT and token-based MAPF-GPT validate faithful reconstruction across distinct learned representations, with up to $3.79\times$ faster policy execution and $2.08\times$ faster online expert-supervised training. Source code is available at https://anonymous.4open.science/r/JointooM.