mlsanityData to 14 Oct 2026

Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization

arXiv 2306.02688 ↗First posted 5 Jun 2023cs.LG stat.MLcited by 23

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Abstract (arXiv)

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

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 35 of its 49 references that are in the map. First posted on arXiv 5 Jun 2023.

Builds on

35 of 49 references.

References · 35

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23 more
  1. 2020Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instancescited by 162
  2. 2022DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problemscited by 129
  3. 2020Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhoodcited by 125
  4. 2022Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimizationcited by 119
  5. 2019Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problemcited by 117
  6. 2021Learning to Delegate for Large-scale Vehicle Routingcited by 109
  7. 2021NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problemcited by 103
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 12; 2025: 4; 2026: 4.

23 papers.

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  1. 2023RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmarkcited by 71
  2. 2024MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Expertscited by 70
  3. 2023Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policycited by 62
  4. 2024Ant Colony Sampling with GFlowNets for Combinatorial Optimizationcited by 53
  5. 2024GOAL: A Generalist Combinatorial Optimization Agent Learnercited by 42
  6. 2023Combinatorial Optimization with Policy Adaptation using Latent Space Searchcited by 41
  7. 2024Learning to Handle Complex Constraints for Vehicle Routing Problemscited by 29
  8. 2024Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectivescited by 23
  9. 2025RRNCO: Towards Real-World Routing with Neural Combinatorial Optimizationcited by 11
  10. 2024Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solvercited by 9
  11. 2024Tackling Prevalent Conditions in Unsupervised Combinatorial Optimization: Cardinality, Minimum, Covering, and Morecited by 7
  12. 2024PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimizationcited by 7
11 more
  1. 2024Moco: A Learnable Meta Optimizer for Combinatorial Optimizationcited by 5
  2. 2025Neural Genetic Search in Discrete Spacescited by 5
  3. 2024Memory-Enhanced Neural Solvers for Routing Problemscited by 4
  4. 2026Survey on Neural Routing Solverscited by 4
  5. 2025Bridging Synthetic and Real Routing Problems via LLM-Guided Instance Generation and Progressive Adaptationcited by 3
  6. 2024Scaling Combinatorial Optimization Neural Improvement Heuristics with Online Search and Adaptationcited by 2
  7. 2026Game-Theoretic Co-Evolution for LLM-Based Heuristic Discoverycited by 2
  8. 2024RL-SPH: Learning to Achieve Feasible Solutions for Integer Linear Programscited by 1
  9. 2025Empowering Targeted Neighborhood Search via Hyper Tour for Large-Scale TSPcited by 1
  10. 2026Test-Time Adaptation for Unsupervised Combinatorial Optimizationcited by 0
  11. 2026Towards Generalization-Oriented Models for Vehicle Routing Problems with Mixture-of-Expertscited by 0