OpenForgeRL: Train Harness-native Agents in Any Environment

TL;DR - OpenForgeRL is an open-source framework for end-to-end reinforcement learning of agents inside stateful inference harnesses such as Codex and OpenClaw. It bridges complex harness rollouts with standard RL stacks while scaling isolated training environments through Kubernetes.

• A lightweight proxy records harness model calls as training data compatible with RL systems such as veRL.
• A Kubernetes orchestrator runs each rollout in a separate remote container, supporting diverse tool-based and multimodal environments.
• Trained agents outperform similarly sized open baselines on most tested claw, browser, and computer-use benchmarks, sometimes matching much larger GUI models.
• RL improves self-verification, tool coverage, and multi-step completion, but error recovery remains weak; learnability also varies substantially by harness.