Agents4d ago
SOLAR Agent Uses Meta-Learning For Lifelong Adaptation
The SOLAR framework treats model weights as an environment for exploration to combat concept drift. By leveraging parameter-level meta-learning, the agent self-improves without the high cost of traditional gradient-based fine-tuning. This approach prevents catastrophic forgetting in non-stationary data streams. Practitioners gain a method for deploying autonomous agents in dynamic, real-world settings.