arXiv AI

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.

arXiv AI

Jun 3

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.

By Han Fang, Paul Weng, Yutong Ban

reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 12

URS: A Unified Neural Routing Solver for Cross-Problem Zero-Shot Generalization

arXiv:2509. 23413v3 Announce Type: replace Abstract: Multi-task neural routing solvers have emerged as a promising paradigm for their ability to solve multiple vehicle routing problems (VRPs) using a single model.

By Changliang Zhou, Canhong Yu, Shunyu Yao, Xi Lin, Zhenkun Wang, Yu Zhou, Qingfu Zhang

rag fine-tuning safety

arXiv AI

Jun 6

CLEAR: Cognition and Latent Evaluation for Adaptive Routing in End-to-End Autonomous Driving

arXiv:2606. 06219v1 Announce Type: cross Abstract: End-to-end autonomous driving models often struggle to balance multi-modal maneuver generation with real-time inference constraints.

By Yining Xing, Zehong Ke, Zhiyuan Liu, Yanbo Jiang, Wenhao Yu, Jianqiang Wang

agents diffusion robotics fine-tuning multimodal benchmarks safety

arXiv AI

1d ago

Geometric Self-Supervised Pre-training for Neural Combinatorial Optimization

arXiv:2608. 00270v2 Announce Type: replace Abstract: Neural Combinatorial Optimization (NCO) techniques have emerged as a highly efficient alternative to traditional exact algorithms for solving routing problems such as the Traveling Salesman Problem (TSP).

By David Aguado, Daniel Fuertes, Carlos R. del-Blanco, Fernando Jaureguizar

computer-vision nlp reinforcement-learning

arXiv Machine Learning

Jul 31

S-GRPO: Unified Post-Training for Large Vision-Language Models

arXiv:2604. 16557v2 Announce Type: replace Abstract: Current post-training methodologies for adapting Large Vision-Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).

By Yuming Yan, Kai Tang, Sihong Chen, Ke Xu, Dan Hu, Qun Yu, Pengfei Hu

llms reinforcement-learning fine-tuning multimodal

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 9

NoRD: A Data-Efficient Vision-Language-Action Model that Drives without Reasoning

arXiv:2602. 21172v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are advancing autonomous driving by replacing modular pipelines with unified end-to-end architectures.

By Ishaan Rawal, Shubh Gupta, Yihan Hu, Wei Zhan

llms agents robotics fine-tuning multimodal safety

arXiv Machine Learning

Jun 16

Think Less, Act Early: Reinforced Latent Reasoning with Early Exit in Vision-Language-Action Models

arXiv:2606. 15099v1 Announce Type: cross Abstract: Existing Vision-Language-Action (VLA) models predominantly rely on explicit Chain-of-Thought (CoT) reasoning to bridge perception and action.

By Dianqiao Lei, Lianlei Shan

reinforcement-learning robotics multimodal benchmarks safety

arXiv AI

Jul 7

Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process

arXiv:2607. 03748v1 Announce Type: new Abstract: Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge.

By Zican Hu, Xuyang Hu, Yiming Liu, Zuwei Long, Wei Liu, Yunzhuo Hao, Jiawei Gu, Linjie Li, Yu Cheng, Zhenhong Sun, Weibo Gu, Xing Sun, Zhi Wang

llms diffusion reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jul 27

Integrated Order Dispatching and Routing for Last-Mile Pickup via Deep Reinforcement Learning

arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.

By Yida Xu, Zhaofang Mao, Yuheng Miao, Jiaxin Zhang, Yiting Sun

reinforcement-learning benchmarks

arXiv AI

Aug 11

OmniV2X: A Generative Foundation Planner for Efficient End-to-End Cooperative Driving

arXiv:2606. 21165v2 Announce Type: replace-cross Abstract: We present OmniV2X, a generative foundation model for vehicle-to-everything (V2X) cooperative driving.

By Juntong Peng, Juanwu Lu, Yupeng Zhou, Can Cui, Yaobin Chen, Ziran Wang

agents fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 28

Learning to Optimize: Joint Routing and Flow Allocation on Sparse Non-Euclidean Networks

arXiv:2607. 23467v1 Announce Type: new Abstract: We study an integrated pickup-and-delivery problem on sparse, non-Euclidean networks that jointly optimizes cyclic routing, cargo flow allocation, and cross-cycle service.

By Haomiao Sun, Fang He, Congyuan Ji, Xindi Tang

reinforcement-learning benchmarks

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