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.
Decision Transformer: Reinforcement Learning via Sequence Modeling
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
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.