ART
ActiveDescription
ART (Agent Reinforcement Trainer) trains multi-step agents for real-world tasks using GRPO reinforcement learning, enabling on-the-job training for models like Qwen, Llama, and more.
Key Features
- GRPO-based reinforcement learning for training multi-step agents on real-world tasks
- W&B Training serverless RL with 40% lower cost and 28% faster training via shared inference cluster
- Pre-built notebooks for email search, 2048, Tic Tac Toe, Codenames, and MCP tool mastery
- SFT distillation support to transfer capabilities from large to smaller models
- Checkpoint-to-inference pipeline with every checkpoint instantly available via W&B Inference
- Supports Qwen, Llama, and other open models with fine-tuning and RL training
Use Cases
💡 Training agents to perform complex multi-step tasks like email search and game playing
💡 Distilling capabilities from frontier models to smaller, deployment-friendly models
💡 Improving agent reliability through on-the-job reinforcement learning
💡 Rapidly iterating on agent behavior with serverless RL infrastructure
Strengths & Limitations
✅ Strengths
- • Actively maintained, recent updates
- • High community interest (10.7k stars)
- • Permissive open-source license (Apache-2.0)
Categories
Quick Start
Install via pip: `pip install openpipe-art`. Try the Colab notebooks for hands-on examples. For W&B serverless RL, set your W&B API key and use `ServerlessBackend` to register a `TrainableModel`. Run training with reward functions defined in Python.