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Machine Learning

OpenForgeRL: Train Harness-native Agents in Any Environment

Xiao Yu, Baolin Peng, Ruize Xu, Hao Zou, Qianhui Wu, Hao Cheng, Wenlin Yao, Nikhil Singh, Zhou Yu, Jianfeng Gao

Featured July 26, 2026

Original Paper

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Simply

OpenForge-RL lets AI agents learn directly how to use complex toolkits in any digital environment by recording their actions and running practice sessions in many separate cloud computers.

In depth
OpenForge-RL provides an open-source framework to train AI agents that use complex inference harnesses directly in their deployment environments. It achieves this by employing a lightweight proxy to intercept and record model interactions as training data, and a Kubernetes orchestrator to run agent rollouts in scalable, remote containers. This decoupling allows end-to-end reinforcement learning for agents using any harness in any environment, overcoming limitations of existing RL frameworks.

Key Takeaways

  • 1
    OpenForge-RL enables end-to-end training of agents using complex inference harnesses by decoupling inference from standard RL training stacks.
  • 2
    The framework utilizes a lightweight proxy for data collection and a Kubernetes orchestrator for scalable, remote rollouts in containerized environments.
  • 3
    The study demonstrates improved agentic reliability through RL training, showing better self-verification and tool coverage across diverse tool-use and GUI environments.

Conceptual Flow

HIGH LEVEL
1
Methodology: Connecting Agents to Learning

The system connects any agent's toolkit to a learning program by watching what the agent does and letting it practice in many separate computer boxes.

Agent's Toolkit
Any Environment
Connect & Observe
Learning Program
Many Practice Boxes
2
Results: Better Agent Performance

This new way of learning helps agents use their tools much better in different computer tasks, even beating bigger, older systems.

Old Agent Learning
New Agent Learning
Compare Performance
Better Tool Use
Higher Success Rates