[PDG 488] OpenClaw-RL: Train Any Agent Simply by Talking

Link to article: https://arxiv.org/abs/2603.10165v2
Title: OpenClaw-RL: Train Any Agent Simply by Talking
Content: OpenClaw-RL is a framework for online RL that lets personal agents improve from the “next-state signals” naturally produced during use, such as user replies, tool outputs, terminal changes, GUI changes, corrections, and feedback. It uses a server–client architecture where deployed agents stream interaction data back to an RL server, while a separate asynchronous server extracts evaluative and directive training signals without slowing inference. Methodologically, it combines broad evaluative feedback with richer but sparser token-level directive supervision, using overlap-guided hint selection and log-probability clipping to stabilize learning across terminal, GUI, software-engineering, and tool-call environments.
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