arXiv AI

ASAP: Exploiting the Satisficing Generalization Edge in Neural Combinatorial Optimization

arXiv:2501. 17377v4 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) has emerged as a promising approach for solving Combinatorial Optimization (CO) problems, such as the 3D Bin Packing Problem (3D-BPP), Traveling Salesman Problem (TSP), or Vehicle Routing Problem (VRP), but these neural solvers often exhibit brittleness when facing distribution shifts.

arXiv AI

Jun 30

Instance-Conditioned Adaptation for Large-scale Generalization of Neural Routing Solver

arXiv:2405. 01906v3 Announce Type: replace Abstract: In modern intelligent transportation systems (ITS), particularly in freight transportation and logistics, real-time route planning is crucial.

By Changliang Zhou, Xi Lin, Zhenkun Wang, Xialiang Tong, Mingxuan Yuan, Qingfu Zhang

efficiency benchmarks

arXiv Machine Learning

Aug 5

Beyond Solving: Prescriptive Probing for Neural Routing Solvers

arXiv:2602. 07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask which stop to drop, which transition to preserve, or which subset of stops to remove if a route is infeasible.

By Reuben Narad, L\'eonard Boussioux, Michael Wagner

reinforcement-learning benchmarks safety

arXiv AI

Jun 2

Learning to Reduce Search Space for Generalizable Neural Routing Solver

arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.

By Changliang Zhou, Xi Lin, Zhenkun Wang, Qingfu Zhang

efficiency

arXiv AI

Jun 4

Smart Transportation Without Neurons -- Fair Metro Network Expansion with Tabular Reinforcement Learning

arXiv:2606. 04167v1 Announce Type: cross Abstract: We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro systems to satisfy travel demand.

By Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos

reinforcement-learning safety

arXiv AI

Jul 23

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

arXiv:2601. 06487v3 Announce Type: replace-cross Abstract: Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on open-ended agent tasks with vast solution spaces (e.

By Qiang Zhang, Boli Chen, Fanrui Zhang, Ruixue Ding, Shihang Wang, Qiuchen Wang, Yinfeng Huang, Haonan Zhang, Rongxiang Zhu, Pengyong Wang, Ailin Ren, Xin Li, Pengjun Xie, Jiawei Liu, Ning Guo, Jingren Zhou, Zheng-Jun Zha

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 2

MViewRouter: Internalizing Geometric Equivariance via Multi-view Alternating Attention for Combinatorial Routing

arXiv:2606. 01084v1 Announce Type: cross Abstract: Combinatorial routing problems such as the Traveling Salesman Problem (TSP) and the Capacitated Vehicle Routing Problem (CVRP) are fundamental NP-hard problems with broad real-world applications.

By Shiyan Liu, Bohan Tan, Yaoxin Wu, Yan Jin

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 16

CADO: From Imitation to Cost Minimization for Heatmap-based Solvers in Combinatorial Optimization

arXiv:2602. 08210v2 Announce Type: replace Abstract: Heatmap-based solvers have emerged as a promising paradigm for Combinatorial Optimization (CO).

By Hyungseok Song, Deunsol Yoon, Kanghoon Lee, Han-Seul Jeong, Soonyoung Lee, Woohyung Lim

diffusion reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jun 16

StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling

arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.

By Jiajun Li, Yu Ding, Shisi Guan, Ran Hou, Wanyuan Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 10

Evolving Parallel Algorithm Portfolios via Potential-Aware Instance Generation with LLMs

arXiv:2608. 06808v1 Announce Type: new Abstract: The Automatic Construction of Portfolios via Large Language Models (LLM-ACP) suffers from poor generalization in practical few-shot scenarios when solving complex combinatorial optimization problems.

By Shaofeng Zhang, Shengcai Liu, Zhiyuan Wang, Ke Tang

llms benchmarks

arXiv AI

Jun 9

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives

arXiv:2601. 01665v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has shown great promise in addressing multi-objective combinatorial optimization problems (MOCOPs).

By Wei Liu, Yaoxin Wu, Yingqian Zhang, Thomas B\"ack, Yingjie Fan

reinforcement-learning safety

arXiv AI

Jun 15

Unsupervised Learning of Efficient Exploration: Pre-training Adaptive Policies via Self-Imposed Goals

arXiv:2601. 19810v2 Announce Type: replace-cross Abstract: Unsupervised pre-training can equip reinforcement learning agents with prior knowledge and accelerate learning in downstream tasks.

By Octavio Pappalardo

agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Jun 10

Latent Guided Sampling for Combinatorial Optimization

arXiv:2506. 03672v2 Announce Type: replace-cross Abstract: Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.

By Sobihan Surendran (LPSM), Adeline Fermanian (LPSM), Sylvain Le Corff (LPSM)

diffusion reinforcement-learning benchmarks

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