Overview
ART (Agent Reinforcement Trainer) is an open-source RL framework by OpenPipe that helps LLM-based agents learn from experience to improve reliability. The project decouples client and server for training, supports local or cloud GPU environments, and provides notebooks and examples for quick experimentation.
Key Features
- RULER (Zero-shot rewards): Use an LLM as a judge to generate reward signals, avoiding manual reward engineering.
- GRPO training loop: Provides a reproducible, scalable training pipeline for multi-step agents and outputs LoRA checkpoints.
- Rich integrations: Compatible with vLLM, Unsloth, and supports monitoring/visualization tools like W&B and Langfuse.
Use Cases
- Improve dialog or tool-using agents via online/offline RL to raise robustness and task completion rates.
- Research and teaching: reproduce experiments, validate reward designs, and explore agent training methods.
- Deploy training outputs as LoRA checkpoints to inference services for fast iteration.
Technical Notes
- Client/server separation: decouples training from inference so training can run on GPU nodes independently.
- LoRA and vLLM support: training outputs are saved as LoRA checkpoints and can be hot-loaded into inference engines.
- Comprehensive notebooks and docs: many examples and benchmarks available to help reproduce results quickly.