OpenForgeRL: Train Harness-native Agents in Any Environment
Merged summary
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.
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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.