You can improve OpenClaw just by using it?

A research paper introduces OpenClaw RL, a framework that enables an AI agent to improve through reinforcement learning by capturing signals from everyday interactions. Instead of relying solely on static training datasets, the agent learns from environment responses such as repeated questions (indicating a poor prior answer), passing unit tests (indicating correct actions), and terminal error messages (hinting at fixes). These signals are converted into RL updates that reward correct behavior, allowing the model to improve simply by being used.