一份基于500篇论文的Agentic RL技术全景与未来

Core Viewpoint - The development of Large Language Models (LLMs) is increasingly focused on enhancing their agentic capabilities through Reinforcement Learning (RL), marking a significant strategic direction for leading AI companies globally [1][2]. Group 1: Transition from Static Generators to Agentic Entities - The rapid integration of LLMs with RL is fundamentally changing the conception, training, and deployment of language models, shifting from viewing LLMs as static generators to recognizing them as agentic entities capable of autonomous decision-making [4][5]. - This new paradigm, termed Agentic Reinforcement Learning (Agentic RL), allows LLMs to operate within sequential decision-making cycles, enhancing their capabilities in planning, reasoning, tool usage, memory maintenance, and self-reflection [5][6]. Group 2: Need for a Unified Framework - Despite the proliferation of research on LLM agents and RL for LLMs, there is a lack of a unified, systematic framework for Agentic RL that integrates theoretical foundations, algorithmic methods, and practical systems [7][8]. - Establishing standardized tasks, environments, and benchmarks is essential for exploring scalable, adaptable, and reliable agentic intelligence [9]. Group 3: Evolution from Preference Tuning to Agentic Learning - Initial training of LLMs relied on behavior cloning and maximum likelihood estimation, but subsequent methods aimed to align model outputs with human preferences, leading to the emergence of agentic reinforcement learning [10][12][14]. - The focus has shifted from optimizing fixed preference datasets to developing agentic RL tailored for specific tasks and dynamic environments, highlighting fundamental differences in assumptions, task structures, and decision granularity [14][19]. Group 4: Key Components of Agentic RL - Agentic RL encompasses several key capabilities, including planning, tool usage, memory, self-improvement, reasoning, and perception, which are interdependent and can be jointly optimized [51]. - The integration of RL into memory management allows agents to dynamically decide what to store, when to retrieve, and how to forget, enhancing their adaptability and self-improvement capabilities [68][75]. Group 5: Tool Usage and Integration - RL has become a critical methodology for evolving tool-using language agents, transitioning from static imitation to dynamic optimization of tool usage in various contexts [61][65]. - Recent advancements in tool-integrated reasoning systems demonstrate the ability of agents to autonomously determine when and how to use tools, adapting to new contexts and unexpected failures [66]. Group 6: Future Directions - The future of agentic planning lies in integrating external search and internal strategy optimization, aiming for a seamless blend of intuitive rapid planning and careful slow reasoning [58]. - There is a growing emphasis on developing structured memory representations that can dynamically control the construction, optimization, and evolution of memory systems, presenting an open and promising direction for enhancing agent capabilities [76].