MAROKO133 Update ai: MIT Researchers Unveil “SEAL”: A New Step Towards Self-Improving AI W

📌 MAROKO133 Eksklusif ai: MIT Researchers Unveil “SEAL”: A New Step Towards Self-I

The concept of AI self-improvement has been a hot topic in recent research circles, with a flurry of papers emerging and prominent figures like OpenAI CEO Sam Altman weighing in on the future of self-evolving intelligent systems. Now, a new paper from MIT, titled “Self-Adapting Language Models,” introduces SEAL (Self-Adapting LLMs), a novel framework that allows large language models (LLMs) to update their own weights. This development is seen as another significant step towards the realization of truly self-evolving AI.

The research paper, published yesterday, has already ignited considerable discussion, including on Hacker News. SEAL proposes a method where an LLM can generate its own training data through “self-editing” and subsequently update its weights based on new inputs. Crucially, this self-editing process is learned via reinforcement learning, with the reward mechanism tied to the updated model’s downstream performance.

The timing of this paper is particularly notable given the recent surge in interest surrounding AI self-evolution. Earlier this month, several other research efforts garnered attention, including Sakana AI and the University of British Columbia’s “Darwin-Gödel Machine (DGM),” CMU’s “Self-Rewarding Training (SRT),” Shanghai Jiao Tong University’s “MM-UPT” framework for continuous self-improvement in multimodal large models, and the “UI-Genie” self-improvement framework from The Chinese University of Hong Kong in collaboration with vivo.

Adding to the buzz, OpenAI CEO Sam Altman recently shared his vision of a future with self-improving AI and robots in his blog post, “The Gentle Singularity.” He posited that while the initial millions of humanoid robots would need traditional manufacturing, they would then be able to “operate the entire supply chain to build more robots, which can in turn build more chip fabrication facilities, data centers, and so on.” This was quickly followed by a tweet from @VraserX, claiming an OpenAI insider revealed the company was already running recursively self-improving AI internally, a claim that sparked widespread debate about its veracity.

Regardless of the specifics of internal OpenAI developments, the MIT paper on SEAL provides concrete evidence of AI’s progression towards self-evolution.

Understanding SEAL: Self-Adapting Language Models

The core idea behind SEAL is to enable language models to improve themselves when encountering new data by generating their own synthetic data and optimizing their parameters through self-editing. The model’s training objective is to directly generate these self-edits (SEs) using data provided within the model’s context.

The generation of these self-edits is learned through reinforcement learning. The model is rewarded when the generated self-edits, once applied, lead to improved performance on the target task. Therefore, SEAL can be conceptualized as an algorithm with two nested loops: an outer reinforcement learning (RL) loop that optimizes the generation of self-edits, and an inner update loop that uses the generated self-edits to update the model via gradient descent.

This method can be viewed as an instance of meta-learning, where the focus is on how to generate effective self-edits in a meta-learning fashion.

A General Framework

SEAL operates on a single task instance (C,τ), where C is context information relevant to the task, and τ defines the downstream evaluation for assessing the model’s adaptation. For example, in a knowledge integration task, C might be a passage to be integrated into the model’s internal knowledge, and τ a set of questions about that passage.

Given C, the model generates a self-edit SE, which then updates its parameters through supervised fine-tuning: θ′←SFT(θ,SE). Reinforcement learning is used to optimize this self-edit generation: the model performs an action (generates SE), receives a reward r based on LMθ′’s performance on τ, and updates its policy to maximize the expected reward.

The researchers found that traditional online policy methods like GRPO and PPO led to unstable training. They ultimately opted for ReST^EM, a simpler, filtering-based behavioral cloning approach from a DeepMind paper. This method can be viewed as an Expectation-Maximization (EM) process, where the E-step samples candidate outputs from the current model policy, and the M-step reinforces only those samples that yield a positive reward through supervised fine-tuning.

The paper also notes that while the current implementation uses a single model to generate and learn from self-edits, these roles could be separated in a “teacher-student” setup.

Instantiating SEAL in Specific Domains

The MIT team instantiated SEAL in two specific domains: knowledge integration and few-shot learning.

• Knowledge Integration: The goal here is to effectively integrate information from articles into the model’s weights.
• Few-Shot Learning: This involves the model adapting to new tasks with very few examples.

Experimental Results

The experimental results for both few-shot learning and knowledge integration demonstrate the effectiveness of the SEAL framework.

In few-shot learning, using a Llama-3.2-1B-Instruct model, SEAL significantly improved adaptation success rates, achieving 72.5% compared to 20% for models using basic self-edits without RL training, and 0% without adaptation. While still below “Oracle TTT” (an idealized baseline), this indicates substantial progress.

For knowledge integration, using a larger Qwen2.5-7B model to integrate new facts from SQuAD articles, SEAL consistently outperformed baseline methods. Training with synthetically generated data from the base Qwen-2.5-7B model already showed notable improvements, and subsequent reinforcement learning further boosted performance. The accuracy also showed rapid improvement over external RL iterations, often surpassing setups using GPT-4.1 generated data within just two iterations.

Qualitative examples from the paper illustrate how reinforcement learning leads to the generation of more detailed self-edits, resulting in improved performance.

While promising, the researchers also acknowledge some limitations of the SEAL framework, including aspects related to catastrophic forgetting, computational overhead, and context-dependent evaluation. These are discussed in detail in the original paper.

Original Paper: https://arxiv.org/pdf/2506.10943

Project Site: https://jyopari.github.io/posts/seal

Github Repo: https://github.com/Continual-Intelligence/SEAL

The post MIT Researchers Unveil “SEAL”: A New Step Towards Self-Improving AI first appeared on Synced.

🔗 Sumber: syncedreview.com

📌 MAROKO133 Eksklusif ai: How context engineering can save your company from AI vi

As cloud project tracking software monday.com’s engineering organization scaled past 500 developers, the team began to feel the strain of its own success. Product lines were multiplying, microservices proliferating, and code was flowing faster than human reviewers could keep up. The company needed a way to review thousands of pull requests each month without drowning developers in tedium — or letting quality slip.

That’s when Guy Regev, VP of R&D and head of the Growth and monday Dev teams, started experimenting with a new AI tool from Qodo, an Israeli startup focused on developer agents. What began as a lightweight test soon became a critical part of monday.com’s software delivery infrastructure, as a new case study released by both Qodo and monday.com today reveals.

“Qodo doesn’t feel like just another tool—it’s like adding a new developer to the team who actually learns how we work," Regev told VentureBeat in a recent video call interview, adding that it has "prevented over 800 issues per month from reaching production—some of them could have caused serious security vulnerabilities."

Unlike code generation tools like GitHub Copilot or Cursor, Qodo isn’t trying to write new code. Instead, it specializes in reviewing it — using what it calls context engineering to understand not just what changed in a pull request, but why, how it aligns with business logic, and whether it follows internal best practices.

"You can call Claude Code or Cursor and in five minutes get 1,000 lines of code," said Itamar Friedman, co-founder and CEO of Qodo, in the same video call interview as with Regev. "You have 40 minutes, and you can't review that. So you need Qodo to actually review it.”

For monday.com, this capability wasn’t just helpful — it was transformative.

Code Review, at Scale

At any given time, monday.com’s developers are shipping updates across hundreds of repositories and services. The engineering org works in tightly coordinated teams, each aligned with specific parts of the product: marketing, CRM, dev tools, internal platforms, and more.

That’s where Qodo came in. The company’s platform uses AI not just to check for obvious bugs or style violations, but to evaluate whether a pull request follows team-specific conventions, architectural guidelines, and historical patterns.

It does this by learning from your own codebase — training on previous PRs, comments, merges, and even Slack threads to understand how your team works.

"The comments Qodo gives aren’t generic—they reflect our values, our libraries, even our standards for things like feature flags and privacy," Regev said. "It’s context-aware in a way traditional tools aren’t."

What “Context Engineering” Actually Means

Qodo calls its secret sauce context engineering — a system-level approach to managing everything the model sees when making a decision.

This includes the PR code diff, of course, but also prior discussions, documentation, relevant files from the repo, even test results and configuration data.

The idea is that language models don’t really “think” — they predict the next token based on the inputs they’re given. So the quality of their output depends almost entirely on the quality and structure of their inputs.

As Dana Fine, Qodo’s community manager, put it in a blog post: “You’re not just writing prompts; you’re designing structured input under a fixed token limit. Every token is a design decision.”

This isn’t just theory. In monday.com’s case, it meant Qodo could catch not only the obvious bugs, but the subtle ones that typically slip past human reviewers — hardcoded variables, missing fallbacks, or violations of cross-team architecture conventions.

One example stood out. In a recent PR, Qodo flagged a line that inadvertently exposed a staging environment variable — something no human reviewer caught. Had it been merged, it might have caused problems in production.

"The hours we would spend on fixing this security leak and the legal issue that it would bring would be much more than the hours that we reduce from a pull-request," said Regev.

Integration into the Pipeline

Today, Qodo is deeply integrated into monday.com’s development workflow, analyzing pull requests and surfacing context-aware recommendations based on prior team code reviews.

“It doesn’t feel like just another tool… It feels like another teammate that joined the system — one who learns how we work," Regev noted.

Developers receive suggestions during the review process and remain in control of final decisions — a human-in-the-loop model that was critical for adoption.

Because Qodo integrated directly into GitHub via pull request actions and comments, Monday.com’s infrastructure team didn’t face a steep learning curve.

“It’s just a GitHub action,” said Regev. “It creates a PR with the tests. It’s not like a separate tool we had to learn.”

“The purpose is to actually help the developer learn the code, take ownership, give feedback to each other, and learn from that and establish the standards," added Friedman.

The Results: Time Saved, Bugs Prevented

Since rolling out Qodo more broadly, monday.com has seen measurable improvements across multiple teams.

Internal analysis shows that developers save roughly an hour per pull request on average. Multiply that across thousands of PRs per month, and the savings quickly reach thousands of developer hours annually.

These aren’t just cosmetic issues — many relate to business logic, security, or runtime stability. And because Qodo’s suggestions reflect monday.com’s actual conventions, developers are more likely to act on them.

The system’s accuracy is rooted in its data-first design. Qodo trains on each company’s private codebase and historical data, adapting to different team styles and practices. It doesn’t rely on one-size-fits-all rules or external datasets. Everything is tailored.

From Internal Tool to Product Vision

Regev’s team was so impressed with Qodo’s impact that they’ve started planning deeper integrations between Qodo and Monday Dev, the developer-focused product line monday.com is building.

The vision is to create a workflow where business context — tasks, tickets, customer feedback — flows directly into the code review layer. That way, reviewers can assess not just whether the code “works,” but whether it solves the right problem.

“Before, we had linters, danger rules, static analysis… rule-based… you need to configure all the rules," Regev said. "But it doesn’t know what you don’t know… Qodo… feels like it’s learning from our engineers.”

This aligns closely with Qodo’s own roadmap. The company doesn’t just review code. It’s building a full platform of developer agents — including Qodo Gen for context-aware code generation, Qodo Merge for automated PR analysis, and Qodo Cover, a regression-testing agent that uses runtime validation to ensure test coverage.

All of this is powered by Qodo’s own infrastructure, including its new open-source embedding model, Qodo-Embed-1-1.5B, which outperformed offerings from OpenAI and Salesforce on code retrieval benchmarks.

What’s Next?

Qodo is now offering its platform under a freemium model — free for individuals, discounted for startups through Google Cloud’s Perks program, and enterprise-grade for companies that need SSO, air-gapped deployment, or advanced controls.

The company is already worki…

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🔗 Sumber: venturebeat.com