Reinforcement learning (RL) is one of the most effective ways to fine-tune an AI agent for reliability, speed, and cost-effectiveness. Until now, getting started meant spending hours or days evaluating GPU providers and wiring up scripts and infrastructure. With our launch of the industry’s first Serverless RL, that’s changed. In this webinar, we’ll introduce the new capabilities and show you how to get your RL training job running in minutes without thinking about GPUs.
What to expect
An overview of Serverless RL followed by a live demo fine-tuning a base LLM for a specific task.
What you will learn
How to write an end-to-end RL training script using the open-source Agent Reinforcement Trainer (ART) framework
One-line reward engineering with ART’s RULER (Relative Universal LLM-Elicited Rewards)—no labeled data, expert feedback, or handcrafted reward functions needed
How to run your RL jobs on a large, managed CoreWeave GPU cluster with instant access and elastic scaling without thinking about infrastructure
Webinar
