MIT's SEAL: Pioneering Self-Adapting AI That Rewrites Its Own Code

This AI Model Never Stops Learning

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. Researchers at Massachusetts Institute of Technology (MIT) have now devised a way for LLMs to keep improving by tweaking their own parameters in response to useful new information. The work is a step toward building artificial intelligence models that learn continually -- a long-standing goal of the field and something that will be crucial if machines are to ever more faithfully mimic human intelligence. In the meantime, it could give us chatbots and other AI tools that are better able to incorporate new information including a user's interests and preferences. The MIT scheme, called Self Adapting Language Models (SEAL), involves having an LLM generate its own synthetic training data based on the input it receives. "The initial idea was to explore if tokens [units of text fed to LLMs and generated by them] could cause a powerful update to a model," says Jyothish Pari, a PhD student at MIT involved with developing SEAL. Pari says the idea was to see if a model's output could be used to train it. Adam Zweiger, an MIT undergraduate researcher involved with building SEAL, adds that although newer models can "reason" their way to better solutions by performing more complex inference, the model itself does not benefit from this reasoning over the long term. SEAL, by contrast, generates new insights and then folds it into its own weights or parameters. Given a statement about the challenges faced by the Apollo space program, for instance, the model generated new passages that try to describe the implications of the statement. The researchers compared this to the way a human student writes and reviews notes in order to aid their learning. The system then updated the model using this data and tested how well the new model is able to answer a set of questions. And finally, this provides a reinforcement learning signal that helps guide the model toward updates that improve its overall abilities and which help it carry on learning. The researchers tested their approach on small and medium-size versions of two open source models, Meta's Llama and Alibaba's Qwen. They say that the approach ought to work for much larger frontier models too. The researchers tested the SEAL approach on text as well as a benchmark called ARC that gauges an AI model's ability to solve abstract reasoning problems. In both cases they saw that SEAL allowed the models to continue learning well beyond their initial training. Pulkit Agrawal, a professor at MIT who oversaw the work, says that the SEAL project touches on important themes in AI, including how to get AI to figure out for itself what it should try to learn. He says it could well be used to help make AI models more personalized. "LLMs are powerful but we don't want their knowledge to stop," he says. SEAL is not yet a way for AI to improve indefinitely. For one thing, as Agrawal notes, the LLMs tested suffer from what's known as "catastrophic forgetting," a troubling effect seen when ingesting new information causes older knowledge to simply disappear. This may point to a fundamental difference between artificial neural networks and biological ones. Pari and Zweigler also note that SEAL is computationally intensive, and it isn't yet clear how best to most effectively schedule new periods of learning. One fun idea, Zweigler mentions, is that, like humans, perhaps LLMs could experience periods of "sleep" where new information is consolidated. Still, for all its limitations, SEAL is an exciting new path for further AI research -- and it may well be something that finds its way into future frontier AI models.

Self-Evolving AI : New MIT AI Rewrites its Own Code and it's Changing Everything

What if artificial intelligence could not only learn but also rewrite its own code to become smarter over time? This is no longer a futuristic fantasy -- MIT's new "self-adapting language models" (SEAL) framework has made it a reality. Unlike traditional AI systems that rely on external datasets and human intervention to improve, SEAL takes a bold leap forward by autonomously generating its own training data and refining its internal processes. In essence, this AI doesn't just evolve -- it rewires itself, mirroring the way humans adapt through trial, error, and self-reflection. The implications are staggering: a system that can independently enhance its capabilities could redefine the boundaries of what AI can achieve, from solving complex problems to adapting in real time to unforeseen challenges. In this exploration by Wes Roth of MIT's innovative SEAL framework, you'll uncover how this self-improving AI works and why it's a fantastic option for the field of artificial intelligence. From its ability to overcome the "data wall" that limits many current systems to its use of reinforcement learning as a feedback mechanism, SEAL introduces a level of autonomy and adaptability that was previously unimaginable. Imagine AI systems that can retain knowledge over time, dynamically adjust to new tasks, and operate with minimal human oversight. Whether you're intrigued by its potential for autonomous robotics, personalized education, or advanced problem-solving, SEAL's ability to rewrite its own rules promises to reshape the future of technology. Could this be the first step toward truly independent, self-evolving AI? The SEAL framework introduces a novel concept of self-adaptation, distinguishing it from traditional AI models. Unlike conventional systems that depend on external datasets for updates, SEAL enables AI to generate synthetic training data independently. This self-generated data is then used to iteratively refine the model, making sure continuous improvement. By persistently updating its internal parameters, SEAL enables AI systems to dynamically adapt to new tasks and inputs. To better illustrate this, consider how humans learn. When faced with a new concept, you might take notes, revisit them, and refine your understanding as you gather more information. SEAL mirrors this process by continuously refining its internal knowledge and performance through iterative self-improvement. This capability allows SEAL to evolve in real time, making it uniquely suited for tasks requiring adaptability and long-term learning. Reinforcement learning plays a critical role in the SEAL framework, acting as a feedback mechanism that evaluates the effectiveness of the model's self-edits. It rewards changes that enhance performance, creating a cycle of continuous improvement. Over time, this feedback loop optimizes the system's ability to generate and apply edits, making sure sustained progress. This process is analogous to how humans learn through trial and error. By rewarding effective changes, SEAL aligns its self-generated data and edits with desired outcomes. The integration of reinforcement learning not only enhances the system's adaptability but also ensures it remains focused on achieving specific goals. This structured feedback mechanism is a cornerstone of SEAL's ability to refine itself autonomously and efficiently. Unlock more potential in self-adapting language models by reading previous articles we have written. SEAL has demonstrated remarkable performance across various applications, particularly in tasks requiring the integration of factual knowledge and advanced question-answering capabilities. For instance, when tested on benchmarks like the ARC AGI, SEAL outperformed other models by effectively generating and using synthetic data. This ability to create its own training material addresses a significant limitation of current AI systems: their reliance on pre-existing datasets. SEAL's capacity for long-term task retention and dynamic adaptation further enhances its utility. It excels in scenarios that demand sustained focus and coherence, such as answering complex questions or adapting to evolving objectives. By using its iterative learning process, SEAL is equipped to handle these challenges with exceptional efficiency, making it a valuable tool for a wide range of real-world applications. One of SEAL's most promising features is its ability to overcome the "data wall" that constrains many AI systems today. By generating synthetic data, SEAL ensures a continuous supply of training material, allowing sustained development without relying on external datasets. This capability is particularly valuable for autonomous AI systems that must operate independently over extended periods. Additionally, SEAL addresses a critical weakness in many current AI models: their struggle with coherence and task retention over long durations. By emulating human learning processes, SEAL enables AI systems to manage complex, long-term tasks with minimal human intervention. This ability to retain and apply knowledge over time positions SEAL as a fantastic tool for advancing AI capabilities. The introduction of SEAL marks a significant milestone in AI research, opening new possibilities for self-improving systems. Its ability to dynamically adapt, retain knowledge, and generate its own training data has far-reaching implications for the future of AI development. Potential applications include: As AI systems become increasingly autonomous and capable of executing complex tasks, frameworks like SEAL will play a crucial role in their evolution. By allowing AI to learn and improve independently, SEAL represents a significant step toward realizing the full potential of artificial intelligence. Its innovative approach to self-adaptation and continuous improvement sets the stage for a new era of AI development, where systems can operate with greater intelligence, flexibility, and autonomy.