AI RESEARCH
OpenClaw-RL: Train Any Agent Simply by Talking
arXiv CS.CL
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ArXi:2603.10165v1 Announce Type: new Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a live, online learning source. We present OpenClaw-RL, a framework built on a simple observation: next-state signals are universal, and policy can problems. They are all interactions that can be used to train the same policy in the same loop.