Self-Adapting Language Models

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Adapting Language Models

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Technology

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

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MIT researchers have developed a framework called Self-Adapting Language Models (SEAL) It enables large language models 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.

SEAL achieved a 72.5% success rate, a dramatic improvement over the 20% rate achieved without RL training and the 0% rate of standard in-context learning.

SEAL takes a non-trivial amount of time to tune the self-edit examples and train the model.

SEAL is not a universal solution, but it can suffer from “catastrophic forgetting”.

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