AINeutralarXiv – CS AI · 7h ago6/10
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Rethinking Agentic Reinforcement Learning In Large Language Models
A new research paper examines the shift from traditional reinforcement learning toward agentic AI systems powered by large language models, where AI agents can autonomously set goals, plan long-term strategies, and adapt dynamically in complex environments. This paradigm moves beyond static, episodic training to incorporate cognitive capabilities like meta-reasoning and self-reflection, representing a fundamental evolution in how RL systems are designed and deployed.