Rethinking Agentic Reinforcement Learning In Large Language Models

arXiv cs.AI / 5/1/2026

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Key Points

The paper argues that traditional reinforcement learning is being reshaped by large language models (LLMs) and open-ended tasks, enabling more agentic RL paradigms.
It describes LLM-based Agentic RL as training autonomous agents that can set goals, plan over the long term, adapt strategies dynamically, and reason interactively under uncertainty.
The work highlights that, unlike conventional RL with static rewards and limited episodic interactions, this approach integrates cognitive-like capabilities (meta-reasoning, self-reflection, multi-step decision-making) into the training loop.
It provides conceptual foundations and methodological innovations, while also surveying key challenges and proposing future research directions for building these agents.

Abstract

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.