Agentic RL: Environments, Trajectories, and the Training Loop

Lightning AI has launched its AI Cloud, a platform that integrates GPUs, datacenter fabric, and hypervisors into a single system to avoid the limitations of traditional VM abstractions. By controlling the entire stack, the service provides deterministic multi-node placement and direct visibility into hardware topology for guest systems. This architecture ensures that performance-critical tools like NCCL and PyTorch function correctly without the need for manual tuning. The AI Cloud is available for both on-demand and spot capacity and is built on the same infrastructure as PyTorch Lightning. Part 12 of the Reinforcement Learning course focuses on establishing a complete training loop for LLM agents that utilize multi-step tool actions. It moves beyond prompt engineering by using trajectories as the primary training unit and implementing RL to reward or penalize specific behaviors. The curriculum introduces RULER for scoring and addresses technical challenges like the credit assignment problem in long-horizon episodes. A practical component demonstrates training a 3B parameter SQL agent on Google Colab using ART and RULER. Hermes agent introduces skill bundles to group multiple related skills into a single command using YAML configuration. This feature streamlines complex workflows, such as coding and testing, by loading all necessary tools and instructions simultaneously. Bundles are designed for resilience, cross-platform compatibility, and easy team sharing via Git repositories. They provide a mechanism for small teams to standardize agent behavior without performance overhead.