We’re developing HarborRL, an open-source framework to democratize agentic RL for everyone. HarborRL makes agentic RL easy to everyone! Building RL infrastructure for agents is complex. HarborRL solves this by unifying model serving, trajectory generation, tool execution, verifier rewards, and policy updates into a single, reproducible pipeline. 🔥 Key Features: Flexible Models × Algorithms × Tasks × Harnesses (MATH): 🤖 Models: Support for Qwen and GLM. 🧠 Algorithms: GRPO and DAPO for reinforcement learning. 🧩 Tasks: 200+ tasks, including Terminal-Bench, SWE-Bench, Deep-SWE, and more. 🛠️ Harnesses: 40+ agent frameworks, including Claude Code, Codex, LangGraph, and SWE-agent.   submitted by   /u/Dear_Treat3688 [link]   [comments]