  Transformers have revolutionized machine learning with their simple yet
effective architecture. Pre-training Transformers on massive text datasets from
the Internet has led to unmatched generalization for natural language
understanding (NLU) tasks. However, such language models remain fragile when
tasked with algorithmic forms of reasoning, where computations must be precise
and robust. To address this limitation, we propose a novel approach that
combines the Transformer's language understanding with the robustness of graph
neural network (GNN)-based neural algorithmic reasoners (NARs). Such NARs
proved effective as generic solvers for algorithmic tasks, when specified in
graph form. To make their embeddings accessible to a Transformer, we propose a
hybrid architecture with a two-phase training procedure, allowing the tokens in
the language model to cross-attend to the node embeddings from the NAR. We
evaluate our resulting TransNAR model on CLRS-Text, the text-based version of
the CLRS-30 benchmark, and demonstrate significant gains over Transformer-only
models for algorithmic reasoning, both in and out of distribution.
