Beyond static AI: MIT’s new framework lets models teach themselves

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Researchers at MIT have developed a framework called Self-Adapting Language Models (SEAL) that enables large language models (LLMs) to continuously learn and adapt by updating their own internal parameters. SEAL teaches an LLM to generate its own training data and update instructions, allowing it to permanently absorb new knowledge and learn new tasks.

This framework could be useful for enterprise applications, particularly for AI agents that operate in dynamic environments, where they must constantly process new information and adapt their behavior.

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