OpenForgeRL: Train Harness-native Agents in Any Environment
OpenForgeRL trains agents inside their deployment harnesses by proxying model calls into standard RL stacks and isolating rollouts in Kubernetes containers; error recovery remains weak.
OpenForgeRL places a lightweight proxy between a harness and its model calls, records those calls for a standard RL stack, and runs each rollout in a remote Kubernetes container. It supports tool agents and GUI agents while keeping inference harnesses separate from training infrastructure.
For builders, the key rethink is to train against the actual harness and environment rather than a simplified chat loop. Reported RL gains include **self-verification**, **tool coverage**, and completion of multi-step plans; harness choice also materially affects learnability.
OpenForgeRL places a lightweight proxy between a harness and its model calls, records those calls for a standard RL stack, and runs each rollout in a remote Kubernetes container. It supports tool agents and GUI agents while keeping inference harnesses separate from training infrastructure. For builders, the key rethink is to train against the actual harness and environment rather than a simplified chat loop. Reported RL gains include **self-verification**, **tool coverage**, and completion of multi-step plans; harness choice also materially affects learnability. Results use hundreds to a few thousand tasks. OpenForgeGUI reports **37.7 on OSWorld-Verified**, **63.0 on Online-Mind2Web**, and **72.3 on WebVoyager**, but cross-benchmark values are not directly comparable, and error recovery remains weak.