Rethinking Agentic Reinforcement Learning In Large Language Models

TL;DR

This paper explores the shift towards agentic reinforcement learning in large language models, emphasizing autonomous goal-setting, planning, and reasoning in complex environments.

Contribution

It provides a comprehensive analysis of the conceptual foundations, methodological innovations, and design principles of LLM-based agentic RL, highlighting future challenges.

Findings

Highlights the integration of cognitive-like capabilities into RL with LLMs

Identifies key challenges in developing autonomous LLM agents

Outlines promising future research directions

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.