Agents4d ago
SOLAR Agent Uses Meta-Learning For Lifelong Adaptation
The SOLAR agent treats its own model weights as an environment for exploration to combat concept drift. It employs parameter-level meta-learning to self-improve without the high costs of traditional gradient-based adaptation. This approach prevents catastrophic forgetting in non-stationary data streams. Practitioners gain a framework for autonomous, continuous model updates in dynamic settings.