MetaClaw Explained: Continuous Learning for LLM Agents with Skills, RL, and Zero-Downtime Adaptation
MetaClaw: Continuous Evolution for AI Agents Without Downtime Modern AI agent platforms like OpenClaw are increasingly expected to handle diverse workloads across dozens of channels. In practice, existing approaches fall into three categories: Storing raw traces without extracting reusable knowledge Maintaining static skill libraries Requiring disruptive retraining cycles with service downtime MetaClaw, takes a different approach. It introduces a continuous meta-learning framework that allows both: The base LLM policy And a reusable skill library to evolve together over time. Instead of relying on a single learning mechanism, MetaClaw combines two complementary loops operating at different time scales: Skill-driven fast adaptation (instant, no gradients) Opportunistic policy optimization (delayed, gradient-based) System Overview MetaClaw maintains a meta-model composed of: θ (theta): base LLM policy parameters S: a library of reusable skill instructions The system improves...