Awesome Agentic: A Curated Reading List of Reinforcement Learning Papers for Large Language Models

Awesome Agentic is a curated list of reinforcement learning papers for large language models maintained by yingyingxia666, sourced from GitHub (link: https://github.com/yingyingxia666/awesome-agentic). The list is categorized into four research directions—Reasoning RL, Agentic RL, Policy Distillation & Drift, and Multi-Agent RL—to help researchers systematically understand the cutting-edge progress in this field.

Against the backdrop of the rapid development of Large Language Models (LLMs), enabling models to possess agent-like thinking, planning, tool usage, and collaboration capabilities has become a research hotspot. The Awesome Agentic project provides a structured academic resource navigation for this field, collecting and categorizing core papers related to LLM reinforcement learning to help readers quickly locate interested literature and establish a cognitive framework for the field. The original author of the project is yingyingxia666, published on GitHub, with update times covering 2024-2025.

Analysis of the Four Research Directions:

1. Reasoning RL: Focuses on enhancing LLM reasoning capabilities. Core issues include chain-of-thought optimization, self-verification and correction, etc. Technical approaches include process supervision, MCTS (Monte Carlo Tree Search), etc., applied in scenarios like mathematical problem solving and code generation.
2. Agentic RL: Focuses on LLM's autonomous action capabilities. Core issues include tool usage, environment interaction, etc. Challenges include sparse rewards, safety alignment, etc. Typical systems include ReAct, AutoGPT, etc.
3. Policy Distillation & Drift: Studies policy transfer and drift handling. Core concepts include policy distillation (knowledge compression) and policy drift (behavior deviation). Technical methods include behavior cloning, inverse reinforcement learning, etc.
4. Multi-Agent RL: Explores multi-agent collaboration/competition. Core issues include collaboration mechanisms, communication learning, etc. Applied in scenarios like multi-role dialogue and software development teams. Challenges include non-stationary environments, credit assignment, etc.

Core Value of the List:

• Systematic Organization: Categorized by topic to help establish a cognitive framework for the field;
• Curated Instead of Piled: Includes papers representing important progress in the field, saving time on screening;
• Continuous Updates: The open-source project is updated as the field develops, supporting community contributions;
• Community-Driven: The GitHub platform gathers community wisdom, allowing paper discussions and sharing of insights.

Resource Usage Guide:

• Beginner Path: First read reviews → Dive deep into a direction → Follow top conferences → Practice hands-on;
• Research Path: Literature research → Technical comparison → Seek inspiration → Build connections;
• Engineering Path: Focus on Agentic RL → Learn tool frameworks → Understand distillation techniques → Explore multi-agent systems.

Field Development Trends:

1. From Single Model to Multi-Agent: Focus shifts to multi-agent collaboration and coordination;
2. From Offline to Online Learning: Emphasis on continuous learning and adaptability;
3. From General-Purpose to Specialized: Optimization in specific fields (code, mathematics, etc.) is gaining attention;
4. From Research to Product: Agentic AI achievements are quickly transformed into practical products (e.g., ChatGPT plugins, AI Agent platforms).

Summary: Awesome Agentic provides structured academic navigation for the LLM reinforcement learning field, helping researchers quickly locate interested topics and systematically understand the development context. Related Resource Recommendations: Awesome-LLM-Agents (comprehensive agent resources), Papers with Code (papers and code), Hugging Face Papers (daily AI papers), Connected Papers (paper citation visualization).