OpenClaw-RL

OpenClaw-RL is an asynchronous reinforcement-learning framework that helps personalize OpenClaw agents using real conversation feedback rather than static offline datasets. It wraps a self-hosted model behind an OpenAI-compatible endpoint, captures multi-turn interactions, and runs rollout collection, reward-model judging, and policy training in parallel so serving stays online while learning continues in the background. The project highlights privacy-conscious operation by keeping model, reward pipeline, and training infrastructure on user-controlled hardware. It supports multiple optimization paths, including binary reward training and on-policy distillation with hindsight hints, making it useful for teams exploring practical agent improvement loops. In OpenClawMap, this fits Infrastructure because it provides foundational runtime/training machinery rather than a packaged end-user assistant. It is best suited to technically capable teams comfortable with GPU infrastructure and experimental RL workflows.

Use this tool if you:
- Want to continuously personalize an OpenClaw agent from real usage feedback.
- Need a training framework that does not block live agent serving.
- Prefer self-hosted RL infrastructure and local control of conversation data.
- Are experimenting with reward-model and distillation-based agent improvement.
- Have GPU resources and engineering capacity for advanced training workflows.