Self-Improvement for Fast, High-Quality Plan Generation
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866910191970156544 |
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| author | Gieselmann, Robert von Huelsen, Henrike Samson, Mihai Meyer, Marie-Christine Piotrowski, Dariusz Radomskyi, Oleksandr Okamoto, Justin Gojayev, Turan Painter, Michael Brown, Gavin Pecora, Federico Wyatt, Jeremy L. |
| author_facet | Gieselmann, Robert von Huelsen, Henrike Samson, Mihai Meyer, Marie-Christine Piotrowski, Dariusz Radomskyi, Oleksandr Okamoto, Justin Gojayev, Turan Painter, Michael Brown, Gavin Pecora, Federico Wyatt, Jeremy L. |
| contents | 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 sub-exponential time. First, we demonstrate that, given optimal data, a decoder-only transformer can generate high-quality plans for unseen problem instances. Second, we show how to self-improve an initial model trained on sub-optimal data. Each round of self-improvement combines multiple model calls with graph search to generate improved plans, used for model fine-tuning. An experimental study on four domains: Blocksworld, Logistics, Labyrinth, and Sokoban, shows on average a 30% reduction in plan length over the source symbolic planner, with over 80% of plans being optimal, where the optimum is known. Plan quality is further improved by inference-time search. The model's latency scales sub-exponentially in contrast to the satisficing and optimal symbolic planners to which we compare. Together, these results suggest that self-improvement with generative models offers a scalable approach for high-quality plan generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_03625 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Self-Improvement for Fast, High-Quality Plan Generation Gieselmann, Robert von Huelsen, Henrike Samson, Mihai Meyer, Marie-Christine Piotrowski, Dariusz Radomskyi, Oleksandr Okamoto, Justin Gojayev, Turan Painter, Michael Brown, Gavin Pecora, Federico Wyatt, Jeremy L. Artificial Intelligence 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 sub-exponential time. First, we demonstrate that, given optimal data, a decoder-only transformer can generate high-quality plans for unseen problem instances. Second, we show how to self-improve an initial model trained on sub-optimal data. Each round of self-improvement combines multiple model calls with graph search to generate improved plans, used for model fine-tuning. An experimental study on four domains: Blocksworld, Logistics, Labyrinth, and Sokoban, shows on average a 30% reduction in plan length over the source symbolic planner, with over 80% of plans being optimal, where the optimum is known. Plan quality is further improved by inference-time search. The model's latency scales sub-exponentially in contrast to the satisficing and optimal symbolic planners to which we compare. Together, these results suggest that self-improvement with generative models offers a scalable approach for high-quality plan generation. |
| title | Self-Improvement for Fast, High-Quality Plan Generation |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2605.03625 |