OpenClaw-RL: A Framework for Online Agent Training via Next-State Signals
Researchers have introduced OpenClaw-RL, a novel reinforcement learning framework designed to optimize AI agents through live, online interactions. Unlike existing systems, OpenClaw-RL leverages 'next-state signals'—such as user replies, tool outputs, or interface changes—as immediate learning sources. The infrastructure utilizes a server-client architecture where an RL server hosts the policy while user terminals stream data via HTTP. An asynchronous server extracts two complementary training signals: evaluative and directive. Methodologically, the framework employs a hybrid RL objective that unifies these signals, using directive signals for rich token-level supervision and evaluative signals for broader availability. To address teacher-student mismatch, it introduces overlap-guided hint selection and log-probability-difference clipping. This approach allows personal agents to improve simply by being used, capturing feedback from corrections and re-queries. Furthermore, it is the first RL framework to unify real-world agent settings across terminal, GUI, software engineering, and tool-call environments, demonstrating significant utility in long-horizon tasks.
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OpenClaw-RL: A Framework for Online Agent Training via Next-State Signals
Researchers have introduced OpenClaw-RL, a novel reinforcement learning framework designed to optimize AI agents through live, online interactions. Unlike existing systems, OpenClaw-RL leverages 'next-state signals'—such as user replies, tool outputs, or interface changes—as immediate learning sources. The infrastructure utilizes a server-client architecture where an RL server hosts the policy while user terminals stream data via HTTP. An asynchronous server extracts two complementary training signals: evaluative and directive. Methodologically, the framework employs a hybrid RL objective that unifies these signals, using directive signals for rich token-level supervision and evaluative signals for broader availability. To address teacher-student mismatch, it introduces overlap-guided hint selection and log-probability-difference clipping. This approach allows personal agents to improve simply by being used, capturing feedback from corrections and re-queries. Furthermore, it is the first RL framework to unify real-world agent settings across terminal, GUI, software engineering, and tool-call environments, demonstrating significant utility in long-horizon tasks.
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