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. It employs parameter-level meta-learning to self-improve without the high cost of traditional gradient-based adaptation. This approach prevents catastrophic forgetting in non-stationary data streams. Practitioners gain a path toward autonomous agents that adapt to real-world shifts without manual curation.