Introduces a framework for LLM agents to autonomously evolve their policies and skill libraries during system idle time without retraining downtime.
arXiv · March 19, 2026 · 2603.17187
The Takeaway
It enables deployed agents to continuously adapt to shifting user tasks in 'the wild' by distilling failure trajectories into reusable skills and performing opportunistic LoRA fine-tuning. This moves beyond static agent architectures toward truly self-improving systems.
From the abstract
Large language model (LLM) agents are increasingly used for complex tasks, yet deployed agents often remain static, failing to adapt as user needs evolve. This creates a tension between the need for continuous service and the necessity of updating capabilities to match shifting task distributions. On platforms like OpenClaw, which handle diverse workloads across 20+ channels, existing methods either store raw trajectories without distilling knowledge, maintain static skill libraries, or require