A recurrent encoder that updates embeddings from prior step embeddings and current state matches a 9-layer recompute-every-step encoder with 3x fewer active layers, cutting latency 1.8-4x on TSP, CVRP, and OP.
Wouda, Leon Lan, Kevin Tierney, and Jinkyoo Park
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Recurrent State Encoders for Efficient Neural Combinatorial Optimization
A recurrent encoder that updates embeddings from prior step embeddings and current state matches a 9-layer recompute-every-step encoder with 3x fewer active layers, cutting latency 1.8-4x on TSP, CVRP, and OP.