ART

Active
GitHub Python Apache-2.0

Description

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

Tags

reinforcement-learning agent-training GRPO llm agent-framework python

Categories

🤖 Agent Framework

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.
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