XiaoRed5/Agentic-RL-Most-Detailed-Intro

What it solves

Getting started with Agentic RL (reinforcement learning for AI agents that use tools and take actions) is confusing because good materials are scattered, code examples are often toy-like, and papers are hard to digest. This project gathers practical knowledge in one place: a visualization dashboard for comparing RL experiments, a runnable implementation of Agentic RL, and a curated set of technical notes on both foundational concepts and recent industry models.

How it works

The repository is organized around three complementary parts.

First, Agent Forge is a visualization project. It lets you take two sets of Agent/RL experiments and put their training trends, offline evaluation results, tool-call costs, and evidence strength into a single interactive dashboard. You can follow the usage guide and download a sample data template to start.

Second, agentic-tau-rl is a code-heavy practical project. It provides a runnable, testable implementation of Agentic RL that covers multi-turn rollout, credit assignment, policy optimization, and behavior shaping. There is also a technical report and a migration guide that explains how to move from offline validation to training on real models.

Third, the technical paper reading section is a set of online articles covering Agentic RL basics, credit assignment, transformer architecture, multi-turn tool calling, and analyses of specific models like LongCat, GLM, and Kimi. These are published as a website, with links listed in the README.

The author, who works on foundation-model pretraining and code-agent training at Meituan LongCat, also shares career advice and invites contributions or questions through GitHub issues.

Who it’s for

This is aimed at people who want to break into LLM base-model work, especially those interested in Agentic RL, tool-use agents, or model post-training. It is useful for students, engineers switching fields, and practitioners looking for concrete code and curated reading material.

Highlights

• Interactive dashboard to compare two RL experiments side by side (training trends, offline eval, tool-call cost, evidence).
• Runnable and testable Agentic RL code covering key stages like rollout, credit assignment, and policy updates.
• A practical migration guide from offline validation to real model training.
• A growing set of technical articles on Agentic RL foundations and analyses of recent industry models (GLM, Kimi, LongCat).
• Open to contributions and questions via GitHub issues.