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Research paper questions effectiveness of excessive planning in AI world models

By PulseAugur Editorial · [1 sources] ·

A new research paper titled "When More Planning Hurts: Learned Planners for World Models" explores the effectiveness of learned planners in world models for robotics. The study, authored by Josef Liyanjun Chen and others, found that a fixed action updater with 1,505 parameters outperformed comparison planners like Adam and iCEM on the PushT benchmark, solving 8 out of 16 scenes. Interestingly, the paper also notes that providing additional updates to the planner did not improve success rates and, in some cases, led to failures. AI

IMPACT This research suggests that over-optimization in AI planning can be detrimental, potentially impacting the design of future AI systems for robotics and other complex decision-making tasks.

RANK_REASON Research paper published on arXiv detailing findings on AI planning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research paper questions effectiveness of excessive planning in AI world models

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Research paper published on arXiv detailing findings on AI planning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Josef Chen ·

    When More Planning Hurts: Learned Planners for World Models

    arXiv:2610.37891v1 Announce Type: cross Abstract: On PushT, our fixed action updater (1,505 parameters) solves 8 of 16 scenes reserved from our configuration selection. The comparison planners perform as follows: Selected Adam (7 tasks solved, total planning and encoding time 218…

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