Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866918005994160128 |
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| author | Gros, Timo P. Müller, Nicola J. Fiser, Daniel Valera, Isabel Wolf, Verena Hoffmann, Jörg |
| author_facet | Gros, Timo P. Müller, Nicola J. Fiser, Daniel Valera, Isabel Wolf, Verena Hoffmann, Jörg |
| contents | Recent work has shown that successful per-domain generalizing action policies can be learned. Scaling behavior, from small training instances to large test instances, is the key objective; and the use of validation instances larger than training instances is one key to achieve it. Prior work has used fixed validation sets. Here, we introduce a method generating the validation set dynamically, on the fly, increasing instance size so long as informative and feasible.We also introduce refined methodology for evaluating scaling behavior, generating test instances systematically to guarantee a given confidence in coverage performance for each instance size. In experiments, dynamic validation improves scaling behavior of GNN policies in all 9 domains used. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior Gros, Timo P. Müller, Nicola J. Fiser, Daniel Valera, Isabel Wolf, Verena Hoffmann, Jörg Machine Learning Artificial Intelligence Recent work has shown that successful per-domain generalizing action policies can be learned. Scaling behavior, from small training instances to large test instances, is the key objective; and the use of validation instances larger than training instances is one key to achieve it. Prior work has used fixed validation sets. Here, we introduce a method generating the validation set dynamically, on the fly, increasing instance size so long as informative and feasible.We also introduce refined methodology for evaluating scaling behavior, generating test instances systematically to guarantee a given confidence in coverage performance for each instance size. In experiments, dynamic validation improves scaling behavior of GNN policies in all 9 domains used. |
| title | Per-Domain Generalizing Policies: On Validation Instances and Scaling Behavior |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.00439 |