2025-11-28
Beyond math and coding: New RL framework helps train LLM agents for complex, real-world tasks
Researchers at the University of Science and Technology of China have developed a new reinforcement learning (RL) framework that helps train large language models (LLMs) for complex agentic tasks beyond well-defined problems such as math and coding.
Their framework, Agent-R1, is compatible with popular RL algorithms and shows considerable improvement on reasoning tasks that require multiple retrieval stages and multi-turn interactions with tools.
The framework is built on a redefinition of the RL paradigm that takes into account the dynamic nature of agentic applications that require int [...]
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