MIT's SEAL Framework: Pioneering Self-Adapting AI Models

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.

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

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more 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. The challenge of adapting LLMs While large language models have shown remarkable abilities, adapting them to specific tasks, integrating new information, or mastering novel reasoning skills remains a significant hurdle. Currently, when faced with a new task, LLMs typically learn from data "as-is" through methods like finetuning or in-context learning. However, the provided data is not always in an optimal format for the model to learn efficiently. Existing approaches don't allow the model to develop its own strategies for best transforming and learning from new information. "Many enterprise use cases demand more than just factual recall -- they require deeper, persistent adaptation," Jyo Pari, PhD student at MIT and co-author of the paper, told VentureBeat. "For example, a coding assistant might need to internalize a company's specific software framework, or a customer-facing model might need to learn a user's unique behavior or preferences over time." In such cases, temporary retrieval falls short, and the knowledge needs to be "baked into" the model's weights so that it influences all future responses. Creating self-adapting language models "As a step towards scalable and efficient adaptation of language models, we propose equipping LLMs with the ability to generate their own training data and finetuning directives for using such data," the MIT researchers state in their paper. The researchers' solution is SEAL, short for Self-Adapting Language Models. It uses a reinforcement learning (RL) algorithm to train an LLM to generate "self-edits" -- natural-language instructions that specify how the model should update its own weights. These self-edits can restructure new information, create synthetic training examples, or even define the technical parameters for the learning process itself. Intuitively, SEAL teaches a model how to create its own personalized study guide. Instead of just reading a new document (the raw data), the model learns to rewrite and reformat that information into a style it can more easily absorb and internalize. This process brings together several key areas of AI research, including synthetic data generation, reinforcement learning and test-time training (TTT). The framework operates on a two-loop system. In an "inner loop," the model uses a self-edit to perform a small, temporary update to its weights. In an "outer loop," the system evaluates whether that update improved the model's performance on a target task. If it did, the model receives a positive reward, reinforcing its ability to generate that kind of effective self-edit in the future. Over time, the LLM becomes an expert at teaching itself. In their study, the researchers used a single model for the entire SEAL framework. However, they also note that this process can be decoupled into a "teacher-student" model. A specialized teacher model could be trained to generate effective self-edits for a separate student model, which would then be updated. This approach could allow for more specialized and efficient adaptation pipelines in enterprise settings. SEAL in action The researchers tested SEAL in two key domains: knowledge incorporation (the ability to permanently integrate new facts) and few-shot learning (the ability to generalize from a handful of examples). For knowledge incorporation, the goal was to see if the model could answer questions about a text passage without having access to the passage during questioning. Finetuning Llama-3.2-1B on the raw text provided only a marginal improvement over the base model. However, when the SEAL model created "self-edits" by generating several "implications" from a passage and was trained on this synthetic data, its accuracy jumped to 47%. Notably, this outperformed results from using synthetic data generated by the much larger GPT-4.1, suggesting the model learned to create superior training material for itself. For few-shot learning, the researchers tested SEAL on examples from the Abstract Reasoning Corpus (ARC), where the model must solve visual puzzles. In the self-edit phase, the model had to generate the entire adaptation strategy, including which data augmentations and tools to use and what learning rate to apply. 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. Implications for the enterprise Some experts project that the supply of high-quality, human-generated training data could be exhausted in the coming years. Progress may soon depend on "a model's capacity to generate its own high-utility training signal," as the researchers put it. They add, "A natural next step is to meta-train a dedicated SEAL synthetic-data generator model that produces fresh pretraining corpora, allowing future models to scale and achieve greater data efficiency without relying on additional human text." For example, the researchers propose that an LLM could ingest complex documents like academic papers or financial reports and autonomously generate thousands of explanations and implications to deepen its understanding. "This iterative loop of self-expression and self-refinement could allow models to keep improving on rare or underrepresented topics even in the absence of additional external supervision," the researchers explain. This capability is especially promising for building AI agents. Agentic systems must incrementally acquire and retain knowledge as they interact with their environment. SEAL provides a mechanism for this. After an interaction, an agent could synthesize a self-edit to trigger a weight update, allowing it to internalize the lessons learned. This enables the agent to evolve over time, improve its performance based on experience, and reduce its reliance on static programming or repeated human guidance. "SEAL demonstrates that large language models need not remain static after pretraining," the researchers write. "By learning to generate their own synthetic self-edit data and to apply it through lightweight weight updates, they can autonomously incorporate new knowledge and adapt to novel tasks." Limitations of SEAL That said, SEAL is not a universal solution. For example, it can suffer from "catastrophic forgetting," where constant retraining cycles can result in the model learning its earlier knowledge. "In our current implementation, we encourage a hybrid approach," Pari said. "Enterprises should be selective about what knowledge is important enough to integrate permanently." Factual and evolving data can remain in external memory through RAG, while long-lasting, behavior-shaping knowledge is better suited for weight-level updates via SEAL. "This kind of hybrid memory strategy ensures the right information is persistent without overwhelming the model or introducing unnecessary forgetting," he said. It is also worth noting that SEAL takes a non-trivial amount of time to tune the self-edit examples and train the model. This makes continuous, real-time editing infeasible in most production settings. "We envision a more practical deployment model where the system collects data over a period -- say, a few hours or a day -- and then performs targeted self-edits during scheduled update intervals," Pari said. "This approach allows enterprises to control the cost of adaptation while still benefiting from SEAL's ability to internalize new knowledge."

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.