Paper
Learning Shortest Paths with Generative Flow Networks
Authors
Nikita Morozov, Ian Maksimov, Daniil Tiapkin, Sergey Samsonov
Abstract
In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties of GFlowNets in non-acyclic environments in relation to shortest paths. We prove that, if the total flow is minimized, forward and backward policies traverse the environment graph exclusively along shortest paths between the initial and terminal states. Building on this result, we show that the pathfinding problem in an arbitrary graph can be solved by training a non-acyclic GFlowNet with flow regularization. We experimentally demonstrate the performance of our method in pathfinding in permutation environments and in solving Rubik's Cubes. For the latter problem, our approach shows competitive results with state-of-the-art machine learning approaches designed specifically for this task in terms of the solution length, while requiring smaller search budget at test-time.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.01786v1</id>\n <title>Learning Shortest Paths with Generative Flow Networks</title>\n <updated>2026-03-02T12:12:13Z</updated>\n <link href='https://arxiv.org/abs/2603.01786v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.01786v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we examine theoretical properties of GFlowNets in non-acyclic environments in relation to shortest paths. We prove that, if the total flow is minimized, forward and backward policies traverse the environment graph exclusively along shortest paths between the initial and terminal states. Building on this result, we show that the pathfinding problem in an arbitrary graph can be solved by training a non-acyclic GFlowNet with flow regularization. We experimentally demonstrate the performance of our method in pathfinding in permutation environments and in solving Rubik's Cubes. For the latter problem, our approach shows competitive results with state-of-the-art machine learning approaches designed specifically for this task in terms of the solution length, while requiring smaller search budget at test-time.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.LG'/>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.AI'/>\n <category scheme='http://arxiv.org/schemas/atom' term='stat.ML'/>\n <published>2026-03-02T12:12:13Z</published>\n <arxiv:primary_category term='cs.LG'/>\n <author>\n <name>Nikita Morozov</name>\n </author>\n <author>\n <name>Ian Maksimov</name>\n </author>\n <author>\n <name>Daniil Tiapkin</name>\n </author>\n <author>\n <name>Sergey Samsonov</name>\n </author>\n </entry>"
}