This Self-Adapting AI Learns by Editing Its Own Brain (SEAL)
AI Summary
This video explains a groundbreaking AI research paper from MIT about SEAL (Self-Adapting Language models), a framework where language models autonomously edit themselves by generating their own training data, updating model weights, and using reinforcement learning to reward improvements. SEAL enables models to internalize new knowledge without manual fine-tuning by creating self-edits as training data and fine-tuning itself. The model showed noteworthy improvements surpassing some synthetic data fine-tuned models. Another experiment tested SEAL’s ability to learn abstract reasoning on ARC benchmark tasks by generating self-edit plans to train itself and improve success rate to 72.5%. However, SEAL faces challenges like catastrophic forgetting, where learning new information risks overwriting older knowledge. The paper discusses potential solutions like reward shaping and constrained edits for future work. Overall, SEAL represents significant progress toward truly self-improving AI, though it is still early days, and managing memory and learning trade-offs is crucial. The video invites viewers to discuss whether SEAL is a real step toward AGI or still in its infancy.