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Hierarchical planning shows mixed results for LeWorldModel control tasks

By PulseAugur Editorial · [2 sources] ·

Researchers have investigated the effectiveness of hierarchical planning in the LeWorldModel for long-horizon goal-conditioned control tasks. Their extension, Hi-LeWM, freezes a pretrained low-level LeWM and adds a high-level planner over latent subgoals. The study found that temporal hierarchy does not inherently improve performance, with mismatches occurring between the high-level action space and search distribution at longer horizons. However, by constraining search and carefully timing subgoal execution, hierarchical regimes were recovered, leading to performance improvements over a flat LeWM. AI

IMPACT This research explores methods to improve AI control capabilities in complex, long-horizon tasks, potentially impacting robotics and autonomous systems.

RANK_REASON The cluster contains a research paper detailing a novel approach to hierarchical planning in a specific AI model.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Hierarchical planning shows mixed results for LeWorldModel control tasks

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The cluster contains a research paper detailing a novel approach to hierarchical planning in a specific AI model.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Niccol\`o Caselli, Salvatore Lo Sardo, Francesco Massafra, Ippokratis Pantelidis, Samuele Punzo, Sathya Kamesh Bhethanabhotla ·

    Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel

    arXiv:2607.12547v1 Announce Type: cross Abstract: We investigate whether temporal hierarchy can improve LeWorldModel on long-horizon goal-conditioned control. We introduce Hi-LeWM, an extension that freezes the pretrained low-level LeWM and adds high-level planning over latent su…

  2. arXiv cs.AI TIER_1 English(EN) · Sathya Kamesh Bhethanabhotla ·

    Mind the Gap: Promises and Pitfalls of Hierarchical Planning in LeWorldModel

    We investigate whether temporal hierarchy can improve LeWorldModel on long-horizon goal-conditioned control. We introduce Hi-LeWM, an extension that freezes the pretrained low-level LeWM and adds high-level planning over latent subgoals. We evaluate Hi-LeWM on PushT and Cube acro…

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