In this hands-on workshop, participants will learn how reinforcement learning (RL) is used to train large language model–based agents that can make sequential decisions, interact with environments, call tools autonomously, and improve performance through experience. 
 
We will cover RL fundamentals for LLM agents, extend Markov Decision Processes (MDPs) to agent settings, explore modular RL frameworks, and dive into practical implementations using OpenPipe’s Agent Reinforcement Trainer (ART). By the end, attendees will understand how to design, train, and evaluate RL-based LLM agents for real-world tasks. 

Prerequisites 

• Familiarity with Large Language Models (LLMs) and Python 

• Basic understanding of Reinforcement Learning concepts (policies, rewards, environments) 

• Prior exposure to agent frameworks is helpful but not required 

*Note: These are tentative details and are subject to change.