  This paper proposes Meta-SAGE, a novel approach for improving the scalability
of deep reinforcement learning models for combinatorial optimization (CO)
tasks. Our method adapts pre-trained models to larger-scale problems in test
time by suggesting two components: a scale meta-learner (SML) and scheduled
adaptation with guided exploration (SAGE). First, SML transforms the context
embedding for subsequent adaptation of SAGE based on scale information. Then,
SAGE adjusts the model parameters dedicated to the context embedding for a
specific instance. SAGE introduces locality bias, which encourages selecting
nearby locations to determine the next location. The locality bias gradually
decays as the model is adapted to the target instance. Results show that
Meta-SAGE outperforms previous adaptation methods and significantly improves
scalability in representative CO tasks. Our source code is available at
https://github.com/kaist-silab/meta-sage
