G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models
Researchers propose G-RRM, a neuro-symbolic approach integrating recurrent reasoning models with symbolic solvers for constraint satisfaction problems. G-RRM guides classical solvers to produce globally correct solutions.
In this work, we focus on SE-RRMs, a symbol-equivariant instantiation of RRMs that exhibits improved extrapolation to larger problem sizes. We propose a neuro-symbolic approach, ``Guiding with Recurrent Reasoning Models'' (G-RRM), which integrates SE-RRMs with symbolic solvers for constraint satisfaction problems. SE-RRMs act as neural solvers that generate full solution proposals and guide classical symbolic solvers, such as backtracking or SAT-based methods like Glucose 4.1 and CaDiCaL 3.0.0, that produce globally correct solutions. Centrally, we investigate when neural guidance with G-RRM i
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