Self Adapting Language Models

Large Language Models, as we know them today, have revolutionized our interaction with technology, demonstrating extraordinary capabilities in understanding and generating language. However, despite their power, these models are inherently static. Once pre-trained on massive amounts of data, they lack built-in mechanisms to autonomously adapt their parameters in response to new tasks, new knowledge, or new examples. This makes it difficult for them to absorb new information or hone new reasoning skills, especially in the absence of specific and abundant data for fine-tuning.
Imagine a human student who, rather than simply passively memorizing information from a textbook, reworks and rewrites it in the form of notes, or perhaps creates visual diagrams, to better understand it and prepare for the exam. This process of assimilating, restructuring, and rewriting data is an integral part of human learning and stands in stark contrast to the “as is” approach typical of current LLM training. SEAL was born to fill this gap. Our ambition is to enable an LLM to self-adapt and continuously evolve, transforming or generating its own training data and its own learning procedure.
The beating heart of SEAL's innovation lies in its ability to make the model generate its own "self-edits", i.e. directives for updating its own weights. Unlike previous approaches that rely on external adaptation modules or auxiliary networks, SEAL directly exploits the generative capacity of the model itself to parameterize and control its own adaptation process.

⁠https://arxiv.org/pdf/2506.10943

Видео Self Adapting Language Models канала Neural Muse