Rethinking Agentic Reinforcement Learning In Large Language Models
For researchers in AI and RL, this paper offers a conceptual framework for integrating LLMs with agentic RL, but it is primarily a survey/position paper without empirical results.
This paper rethinks reinforcement learning in the context of large language models, proposing an agentic paradigm that integrates cognitive capabilities like meta-reasoning and self-reflection for autonomous goal-setting and long-term planning. It provides a conceptual analysis and outlines future directions.
Reinforcement Learning (RL) has traditionally focused on training specialized agents to optimize predefined reward functions within narrowly defined environments. However, the advent of powerful Large Language Models (LLMs) and increasingly complex, open-ended tasks has catalyzed a paradigm shift towards agentic paradigms within RL. This emerging framework extends beyond traditional RL by emphasizing the development of autonomous agents capable of goal-setting, long-term planning, dynamic strategy adaptation, and interactive reasoning in uncertain, real-world environments. Unlike conventional approaches that rely heavily on static objectives and episodic interactions, LLM-based Agentic RL incorporates cognitive-like capabilities such as meta-reasoning, self-reflection, and multi-step decision-making directly into the learning loop. In this paper, we provide a deep insight for looking the conceptual foundations, methodological innovations, and effective designs underlying this trend. Furthermore, we identify critical challenges and outline promising future directions for building LLM-based Agentic RL.