Published on December 24, 2025, this summary presents Agent-R1, an end-to-end reinforcement learning framework for training LLM agents in multi-turn interactions with tools and environments.
Published on December 24, 2025, the material presents Agent-R1 as a framework for end-to-end reinforcement learning to train LLM agents in multi-turn interactions. Its stated scope includes agents that use tools and interact sequentially with environments across multiple rounds.
The summary provides no quantitative results, evaluation details, authors, or publication venue. To check the original work, consult the record associated with this bookmark and verify whether the article describes its method, experiments, and results beyond the brief scope reported here.