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Generative models achieve high-quality plan generation via self-improvement

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a self-improvement technique for generative models to produce high-quality plans more efficiently. This method involves fine-tuning an initial model with improved plans generated through a combination of model calls and graph search. Experiments across four domains demonstrated an average 30% reduction in plan length compared to traditional symbolic planners, with over 80% of generated plans being optimal. AI

IMPACT This self-improvement approach could lead to more efficient and higher-quality AI-driven planning systems across various applications.

RANK_REASON The cluster contains an academic paper detailing a new method for AI plan generation.

Read on arXiv cs.AI →

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

Generative models achieve high-quality plan generation via self-improvement

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The cluster contains an academic paper detailing a new method for AI plan generation.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Robert Gieselmann, Henrike von Huelsen, Mihai Samson, Marie-Christine Meyer, Dariusz Piotrowski, Oleksandr Radomskyi, Justin Okamoto, Turan Gojayev, Michael Painter, Gavin Brown, Federico Pecora, Jeremy L. Wyatt ·

    Self-Improvement for Fast, High-Quality Plan Generation

    arXiv:2605.03625v1 Announce Type: new Abstract: Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality solution. We address the challenge of producing high-qu…

  2. arXiv cs.AI TIER_1 English(EN) · Jeremy L. Wyatt ·

    Self-Improvement for Fast, High-Quality Plan Generation

    Generative models trained on synthetic plan data are a promising approach to generalized planning. Recent work has focused on finding any valid plan, rather than a high-quality solution. We address the challenge of producing high-quality plans, a computationally hard problem, in …

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