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MIT’s SEAL Enables AI To Self-Teach And Continuously Adapt

• Written by Kiara Fabbri Former Tech News Writer
• Fact-Checked by Sarah Frazier Former Content Manager

MIT’s Improbable AI Lab researchers developed an innovative framework called SEAL (Self-Adapting LLMs). SEAL enables artificial intelligence systems to learn new skills after their initial training through self-teaching.

In a rush? Here are the quick facts:

• MIT developed SEAL, a framework that allows AI to teach itself new skills.
• SEAL rewrites its own training using self-generated summaries, quizzes, and notes.
• It achieved 40% better recall and 72.5% success in reasoning tasks.

The new system surpasses existing large language models, including ChatGPT, which the researchers explain generally remain fixed once trained and require extensive retraining to learn new information.

SEAL’s main breakthrough lies in its ability to let the AI generate its own training materials and then use these materials to fine-tune itself. Indeed, when presented with new data—such as a news article or example tasks—the system creates simplified explanations, related facts, or practice questions.

The researchers explain that this process mimics human learning methods, where people typically write notes and flashcards to improve their understanding and retention of new information.

During the “inner loop’’ phase, SEAL executes a small update known as a “self-edit.” The system conducts an “outer loop’’ assessment to verify the improvement of its performance after the update. When the AI identifies a positive change, it remembers the modification; otherwise, it attempts new approaches.

The researchers say that this iterative process allows the AI to continuously improve its knowledge and adapt to new challenges without the need for full retraining.

The researchers note that the method used by SEAL stands apart from all traditional reinforcement learning (RL) approaches. Agents in standard RL environments learn through trial-and-error to maximize their rewards when they interact with their surroundings.

SEAL uses RL as a tool to train its AI system to create and implement its own learning resources, which makes the language model as both an instructor and a student. The system produces “self-edits,” which include personalized instructions together with synthetic data for its refinement process. This approach enables persistent, self-directed updates that conventional RL methods, which do not adjust model parameters directly, cannot achieve.

MIT researchers tested SEAL on two fronts. First, in learning new facts, SEAL transformed raw text into implications and Q&A formats, resulting in a 47% accuracy improvement that surpassed even GPT-4.1’s training materials. Second, in abstract reasoning tasks, SEAL achieved a 72.5% success rate, outperforming models without reinforcement learning or standard training.

The researchers argue that the potential applications are wide-ranging. SEAL may enables various applications ranging from customized AI tutors, to self-evolving research collaborators, and autonomous agents that improve through experience.

The technology has the potential to transform enterprise AI systems by letting them develop internal thinking capabilities, which replace the need for costly retraining processes to handle fast data changes and user requirements.

However, challenges remain, for instance, SEAL can suffer from what the researchers call ‘‘catastrophic forgetting’’, where new learning overwrites previous knowledge, and the self-editing process requires significant computing resources.

To mitigate these issues, the team proposes hybrid systems that combine SEAL with external tools for temporary memory, reserving SEAL’s updates for essential, long-term knowledge.

Despite these hurdles, the MIT researchers believe that this technology will help machines achieve human-like adaptability, and lifelong learning abilities.

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Dating App Bumble Plans Major Layoffs To Boost Growth

• Written by Kiara Fabbri Former Tech News Writer
• Fact-Checked by Sarah Frazier Former Content Manager

Bumble plans to reduce its workforce by 30% to save $40 million annually, and CEO Whitney Wolfe Herd will take charge of leading growth initiatives.

In a rush? Here are the quick facts:

• Bumble plans to cut 30% of its workforce, about 240 jobs.
• Layoffs expected to save Bumble $40 million annually.
• Bumble’s market value dropped from $7.7 billion to $538 million.

Bumble stock prices increased by 26% after the dating application company revealed its plan to eliminate 240 positions, which represents 30% of its workforce. The company predicts these layoffs will produce yearly savings of $40 million, as first reported by CNBC .

Bumble expects to spend between $13 million and $18 million on workforce reduction expenses, through severance payments and benefits, during the third and fourth quarters of 2025, as reported by CNBC. Despite these upfront costs, the company aims to strengthen its core business and invest the savings into new product and technology development.

A Bumble spokesperson said to CNBC, “Our focus now is on moving forward in a way that strengthens our core business, continues to serve our members effectively, and positions us for future growth.” The company described the layoffs as part of a plan to realign its operating structure to better focus on strategic priorities.

CNBC reports that Bumble’s shares had struggled since going public in 2021, with its market value dropping from $7.7 billion to about $538 million. Founder Whitney Wolfe Herd, who stepped down as CEO early last year, returned to lead the company earlier this year.

Along with the layoffs, Bumble updated its financial forecast for the current quarter. It now expects revenue between $244 million and $249 million and adjusted EBITDA of $88 million to $93 million, higher than its earlier forecast, as noted by CNBC.

The company plans to follow local laws and consultation requirements during the workforce reduction, which could extend beyond 2025’s fourth quarter in some countries.

This move comes amid a broader wave of tech companies reducing staff, driven in large part by the growing impact of AI on workforce dynamics . Across the industry, companies are restructuring and cutting jobs, often citing the need for greater efficiency as AI technologies automate more tasks.

For example, Meta recently faced criticism after laying off about 3,600 employees , roughly 5% of its workforce, including some who were on parental or medical leave, sparking concerns about fairness and transparency.

As AI increasingly takes on roles traditionally performed by humans, tech firms are using these efficiency drives to justify workforce reductions, reshaping corporate cultures and raising questions about the future job security of tech workers.