Papers › Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer

Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer

10 Jul 2023arXiv:2307.04895archive 2025-07-28

Zhun Yang, Adam Ishay, Joohyung Lee

Constraint satisfaction problems (CSPs) are about finding values of variables that satisfy the given constraints. We show that Transformer extended with recurrence is a viable approach to learning to solve CSPs in an end-to-end manner, having clear advantages over state-of-the-art methods such as Graph Neural Networks, SATNet, and some neuro-symbolic models. With the ability of Transformer to handle visual input, the proposed Recurrent Transformer can straightforwardly be applied to visual constraint reasoning problems while successfully addressing the symbol grounding problem. We also show how to leverage deductive knowledge of discrete constraints in the Transformer's inductive learning to achieve sample-efficient learning and semi-supervised learning for CSPs.

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