2 citations · 2 across the 6 of their papers we have counts for
9 papers
GraphSkillEvo: Evolutionary Optimization of Graph-Structured Agent Skills
Rui Sun, Zhi Zheng, Zhenkun Wang +1
Skills can improve the performance of Large Language Model (LLM) agents by providing task-specific procedural guidance, while skill optimization further improves their effectivenes…
Agentic ESOpt: Fine-Tuning Long-Horizon LLM Agents with Minimal GPU Requirements
Zhi Zheng, Rongsheng Chen, Yunpeng Ba +3
Reinforcement Learning (RL) has been promising in single-turn LLM fine-tuning. However, long-horizon agentic reasoning introduces increasingly branching interactions and sparse rew…
SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver
Rongsheng Chen, Changliang Zhou, Canhong Yu +4
Generalist neural routing solvers have shown great potential in solving diverse vehicle routing problems (VRPs) with a unified model. However, existing solvers are typically limite…
Rethinking Constraint Awareness for Efficient State Embedding of Neural Routing Solver
Canhong Yu, Changliang Zhou, Rongsheng Chen +2
Heavy-Encoder-Light-Decoder (HELD) neural routing solvers have emerged as a promising paradigm due to their broad applicability across multiple vehicle routing problems (VRPs). How…
Efficient Decoder Scaling Strategy for Neural Routing Solvers
Qing Luo, Fu Luo, Ke Li +1
Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent s…
MTL-KD: Multi-Task Learning Via Knowledge Distillation for Generalizable Neural Vehicle Routing Solver
Yuepeng Zheng, Fu Luo, Zhenkun Wang +2
Multi-Task Learning (MTL) in Neural Combinatorial Optimization (NCO) is a promising approach to train a unified model capable of solving multiple Vehicle Routing Problem (VRP) vari…
Improving Generalization of Neural Combinatorial Optimization for Vehicle Routing Problems via Test-Time Projection Learning
Yuanyao Chen, Rongsheng Chen, Fu Luo +1
Neural Combinatorial Optimization (NCO) has emerged as a promising learning-based paradigm for addressing Vehicle Routing Problems (VRPs) by minimizing the need for extensive manua…
Rethinking Neural Combinatorial Optimization for Vehicle Routing Problems with Different Constraint Tightness Degrees
Fu Luo, Yaoxin Wu, Zhi Zheng +1
Recent neural combinatorial optimization (NCO) methods have shown promising problem-solving ability without requiring domain-specific expertise. Most existing NCO methods use train…
Dynamic Multi-objective Ensemble of Acquisition Functions in Batch Bayesian Optimization
Jixiang Chen, Fu Luo, Zhenkun Wang
Bayesian optimization (BO) is a typical approach to solve expensive optimization problems. In each iteration of BO, a Gaussian process(GP) model is trained using the previously eva…