OpenForgeRL: Train Harness-native Agents in Any Environment
Quick summary
arXiv:2607.21557v3 Announce Type: replace Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems. While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express stateful, multi-process harness inference. To address this, we present OpenForgeRL, an open-source framework for training harness-based agents end-to-end in diverse environments. OpenForgeRL achieves this with a lightweight pro
Key takeaways
- arXiv:2607.21557v3 Announce Type: replace Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems.
- While powerful, these complex harnesses also make agents hard to train end-to-end with open infrastructure, whose SFT/RL stacks cannot natively express stateful, multi-process harness inference.
- To address this, we present OpenForgeRL, an open-source framework for training harness-based agents end-to-end in diverse environments.
Why it matters
“OpenForgeRL: Train Harness-native Agents in Any Environment” exposes the compute, energy and supply-chain layer behind model competition. Capacity shifts can influence model costs, service availability and the ability of smaller companies to compete.
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