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cs.RO2025
Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse
Zeyuan Zhao, Chaoran Li, Shao Zhang +1
Multi-Agent Pickup and Delivery (MAPD) is a challenging extension of Multi-Agent Path Finding (MAPF), where agents are required to sequentially complete tasks with fixed-location p…
cs.LG2025
Context Value Informed In-Context Reinforcement Learning
Wenhao Zhang, Shao Zhang, Xihuai Wang +2
In-context reinforcement learning (ICRL) has emerged as an effective paradigm for test time adaptation to unseen tasks without parameter updates. However, existing ICRL methods can…
cs.CL2025
Structured In-context Environment Scaling for Large Language Model Reasoning
Peng Yu, Zeyuan Zhao, Shao Zhang +3
Large language models (LLMs) have achieved significant advancements in reasoning capabilities through reinforcement learning (RL) via environmental exploration. As the intrinsic pr…