HiMAC decomposes LLM agent tasks into macro planning and micro execution using critic-free hierarchical RL and iterative co-evolution, outperforming baselines on ALFWorld, WebShop, and Sokoban.
Advances in neural information processing systems31(2018)
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Composing a policy that maps 2D waypoints to joint actions with a frozen world model yields a lifted world model that achieves 3.8 times lower mean joint error than direct low-level search while being more compute-efficient and generalizing to unseen environments.
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HiMAC: Hierarchical Macro-Micro Learning for Long-Horizon LLM Agents
HiMAC decomposes LLM agent tasks into macro planning and micro execution using critic-free hierarchical RL and iterative co-evolution, outperforming baselines on ALFWorld, WebShop, and Sokoban.
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Lifting Embodied World Models for Planning and Control
Composing a policy that maps 2D waypoints to joint actions with a frozen world model yields a lifted world model that achieves 3.8 times lower mean joint error than direct low-level search while being more compute-efficient and generalizing to unseen environments.