Learning to Search with Subgoals
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F25%3A00388665" target="_blank" >RIV/68407700:21730/25:00388665 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-3-032-02725-2_24" target="_blank" >https://doi.org/10.1007/978-3-032-02725-2_24</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-032-02725-2_24" target="_blank" >10.1007/978-3-032-02725-2_24</a>
Alternative languages
Result language
angličtina
Original language name
Learning to Search with Subgoals
Original language description
This work investigates whether a Transformer-based language model can learn to imitate a problem-solving process that decomposes tasks into subgoals, akin to human cognitive strategies. We train the model to replicate a solver that employs a greedy approach, switching to subproblems upon encountering obstacles. Using two synthetic tasks—the Countdown arithmetic puzzle and a Reachability with Obstacles pathfinding task—we demonstrate successful imitation of a simple solver, with generalization to unseen input samples and solution path lengths. We evaluate several variants of the Pythia model, finding that even a compact model (310k parameters) performs competitively, though larger models converge faster. Our results suggest that even small language models can internalize structured, hierarchical problem-solving, highlighting their potential for understanding how human-like subgoal decomposition can be implemented with neural networks.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Advances in Computational Intelligence 18th International Work-Conference on Artificial Neural Networks, IWANN 2025, A Coruña, Spain, June 16–18, 2025, Proceedings, Part I
ISBN
978-3-032-02724-5
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
12
Pages from-to
310-321
Publisher name
Springer
Place of publication
Cham
Event location
A Coruña
Event date
Jun 16, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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