2 citations · 2 across the 6 of their papers we have counts for
5 papers · 1 filter
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…