Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning 事件

Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning arXiv:2511.14460v2 Announce Type: replace Abstract: Large language models (LLMs) have rapidly evolved from single-turn text generators into the foundation of increasingly capable agents. As these agents take on more complex reasoning, decision making, tool use, and long-horizon tasks, reinforcement learning (RL) is becoming increasingly important for shaping their behavior. This shift is especially visible in agentic R