Agentic RL
As Large Language Models (LLMs) achieve breakthroughs in natural language processing, their applications continue to expand. However, they also exhibit limitations such as knowledge cutoffs, hallucinations, and deficiencies in complex computation and logical reasoning. To address these challenges, Agentic RL, which combines agents with Reinforcement Learning (RL), is emerging as a key research direction. Agentic RL enables LLMs to possess capabilities like autonomous planning, decision-making, tool use, and environmental interaction by creating a closed-loop interaction with the external world (e.g., search engines, code interpreters, databases, browsers) and continuously optimizing through reward signals. In practical applications, it not only understands requirements and plans autonomously but also constantly corrects and optimizes within an execution-feedback loop. ...