Training Agents with Reinforcement Learning
Hosted by Will Brown
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Go deeper with a course
What you'll learn
A crash course in RL for LLMs
Overview of RL basics: policies, actions, environments, rewards, how it relates to LLMs via GRPO
How to build "RL-ready" agents
Walkthrough of building a search agent + how to prototype environments via tool integrations + offline evaluations
Tips and tricks for RL training
Recipes for improving your agent via online RL training with GRPO
Why this topic matters
2025 is the Year of the Agents, everyone's talking about RL, but it might seem complicated and inaccessible. It doesn't have to be. RL gives you a set of tools for actually improving your agent models to be performant and cost-effective without relying on closed-source API models.
You'll learn from
Will Brown
Research Lead at Prime Intellect
Will is a Research Lead at Prime Intellect, working on advancing the frontier of open-source agentic RL. He was previously a Machine Learning Researcher at Morgan Stanley and an Applied Scientist at AWS, and completed a PhD in Computer Science at Columbia University focused on multi-agent learning.
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