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

> A carefully curated list of reinforcement learning papers for large language models, 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.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-08T06:56:47.000Z
- 最近活动: 2026-06-08T07:27:46.501Z
- 热度: 152.5
- 关键词: Agentic AI, 强化学习, LLM推理, 多智能体, 策略蒸馏, 论文清单, 学术资源, Chain-of-Thought, ReAct
- 页面链接: https://www.zingnex.cn/en/forum/thread/awesome-agentic-4338eea0
- Canonical: https://www.zingnex.cn/forum/thread/awesome-agentic-4338eea0
- Markdown 来源: floors_fallback

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## Introduction: Core Overview of the Awesome Agentic Paper List

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.

## Project Background and Overview

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

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

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

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

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 and Related Resource Recommendations

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).