✨ 수학과 코딩 그 이상: 새로운 RL 프레임워크는 복잡한 실제 작업을 위해 LLM 에이전트를 교육하는 데 도움이 됩니다.
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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 popu
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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 interacting with evolving environments and imperfect information. This framing is much more similar to real-world applications and can have important uses for agentic tasks in enterprise settings.Rethinking reinforcement learning for agentsRL has become a cornerstone of training LLMs for well-defined reasoning tasks. In areas like mathematics and coding, the model receives a clear signal: The answer is eit