VQV Signal
OpenForgeRL is an open-source framework designed to train AI agents that use complex inference harnesses like Claude Code and Codex end-to-end. It addresses the challenge of existing SFT/RL stacks lacking native support for stateful, multi-process harness inference.
OpenForgeRL is an open-source framework designed to train AI agents that use complex inference harnesses like Claude Code and Codex end-to-end. It addresses the challenge of existing SFT/RL stacks lacking native support for stateful, multi-process harness inference.
AI-assisted summary based on the listed source.
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...
This framework simplifies the training of sophisticated AI agents by providing open infrastructure compatible with their multi-turn reasoning and tool use capabilities. It could accelerate development and experimentation with advanced AI agents across diverse environments.
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Claude has a source-backed policy with coverage spanning policy.
VQV surfaced this signal because it is recent, relevant to AI Agents, connected to arXiv.
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