ART is a platform for agentic training, providing a reinforcement learning framework, training environment, and compute orchestrator. It enables the improvement of multi-step agent reasoning and tool usage through group relative policy optimization and a judge-based reward modeling system.
The project features tools for model distillation to transfer capabilities from large teacher models to smaller architectures, as well as a system for capturing execution trajectories to generate synthetic training data. It supports specialized training workflows including supervised fine-tuning for baseline establishment and the creation of reproducible task scenarios.
The infrastructure manages GPU compute resources via ephemeral environment provisioning and hybrid local-remote execution. It includes capabilities for trajectory-based data capture, model checkpoint management, and the routing of low-rank adaptations for inference.
The system provides observability through agent workflow scoring, compute cost monitoring, and training metric tracking.
