verl-f15f34b8·2 events·first seen Aliases: veRL
OpenForgeRL is an open-source framework that enables end-to-end reinforcement learning training of AI agents operating within real inference harnesses (e.g., Claude Code, Codex, OpenClaw) and diverse environments. It uses a lightweight proxy to record harness model calls as training data for standard RL codebases like veRL, plus a Kubernetes orchestrator for scalable rollouts in isolated containers. Trained agents (OpenForgeClaw and OpenForgeGUI) achieve competitive results on benchmarks including ClawEval, OSWorld-Verified, Online-Mind2Web, and WebVoyager, matching or surpassing models several times larger in GUI tasks. The work also analyzes how harness choice and RL shape agent behavior, finding meaningful variation in learnability across harnesses.
A Hugging Face blog post surveys 16 open-source reinforcement learning libraries for LLM training, analyzing their architectural approaches to async and synchronous token generation pipelines. The piece distills practical lessons about throughput, scalability, and design trade-offs across the ecosystem. It serves as a comparative landscape analysis for practitioners building or choosing RL training infrastructure for language models.