Learning and Reusing Policy Decompositions for Hierarchical Generalized Planning

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

arXiv:2603.19685v1 Announce Type: new Abstract: Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile

arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit

arXiv:2605.26371v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) promises to solve long-horizon Reinforcement Learning (RL) tasks more efficiently than

arXiv:2605.21902v1 Announce Type: new Abstract: Growing attention to intelligent agents has put a spotlight on one of their central capabilities: planning. Early attempts to

arXiv:2605.00412v1 Announce Type: new Abstract: World models have recently re-emerged as a central paradigm for embodied intelligence, robotics, autonomous driving, and

arXiv:2610.21108v1 Announce Type: new Abstract: LLM-based planning agents commit to plans of different lengths before interacting with a partially observable environment. Some

arXiv:2605.15975v1 Announce Type: new Abstract: We tackle the challenge of building embodied AI agents that can reliably solve long-horizon planning problems. Imitation learning