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cs.AI2025
CuES: A Curiosity-driven and Environment-grounded Synthesis Framework for Agentic RL
Shinji Mai, Yunpeng Zhai, Ziqian Chen +5
Large language model based agents are increasingly deployed in complex, tool augmented environments. While reinforcement learning provides a principled mechanism for such agents to…
cs.AI2025
AgentScope 1.0: A Developer-Centric Framework for Building Agentic Applications
Dawei Gao, Zitao Li, Yuexiang Xie +20
Driven by rapid advancements of Large Language Models (LLMs), agents are empowered to combine intrinsic knowledge with dynamic tool use, greatly enhancing their capacity to address…