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

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

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 Hot ai: Black Forest Labs launches Flux.2 AI image models to challenge

It's not just Google's Gemini 3, Nano Banana Pro, and Anthropic's Claude Opus 4.5 we have to be thankful for this year around the Thanksgiving holiday here in the U.S.

No, today the German AI startup Black Forest Labs released FLUX.2, a new image generation and editing system complete with four different models designed to support production-grade creative workflows.

FLUX.2 introduces multi-reference conditioning, higher-fidelity outputs, and improved text rendering, and it expands the company’s open-core ecosystem with both commercial endpoints and open-weight checkpoints.

While Black Forest Labs previously launched with and made a name for itself on open source text-to-image models in its Flux family, today's release includes one fully open-source component: the Flux.2 VAE, available now under the Apache 2.0 license.

Four other models of varying size and uses — Flux.2 [Pro], Flux.2 [Flex], and Flux.2 [Dev] —are not open source; Pro and Flex remain proprietary hosted offerings, while Dev is an open-weight downloadable model that requires a commercial license obtained directly from Black Forest Labs for any commercial use. An upcoming open-source model is Flux.2 [Klein], which will also be released under Apache 2.0 when available.

But the open source Flux.2 VAE, or variational autoencoder, is important and useful to enterprises for several reasons. This is a module that compresses images into a latent space and reconstructs them back into high-resolution outputs; in Flux.2, it defines the latent representation used across the multiple (four total, see blow) model variants, enabling higher-quality reconstructions, more efficient training, and 4-megapixel editing.

Because this VAE is open and freely usable, enterprises can adopt the same latent space used by BFL’s commercial models in their own self-hosted pipelines, gaining interoperability between internal systems and external providers while avoiding vendor lock-in.

The availability of a fully open, standardized latent space also enables practical benefits beyond media-focused organizations. Enterprises can use an open-source VAE as a stable, shared foundation for multiple image-generation models, allowing them to switch or mix generators without reworking downstream tools or workflows.

Standardizing on a transparent, Apache-licensed VAE supports auditability and compliance requirements, ensures consistent reconstruction quality across internal assets, and allows future models trained for the same latent space to function as drop-in replacements.

This transparency also enables downstream customization such as lightweight fine-tuning for brand styles or internal visual templates—even for organizations that do not specialize in media but rely on consistent, controllable image generation for marketing materials, product imagery, documentation, or stock-style visuals.

The announcement positions FLUX.2 as an evolution of the FLUX.1 family, with an emphasis on reliability, controllability, and integration into existing creative pipelines rather than one-off demos.

A Shift Toward Production-Centric Image Models

FLUX.2 extends the prior FLUX.1 architecture with more consistent character, layout, and style adherence across up to ten reference images.

The system maintains coherence at 4-megapixel resolutions for both generation and editing tasks, enabling use cases such as product visualization, brand-aligned asset creation, and structured design workflows.

The model also improves prompt following across multi-part instructions while reducing failure modes related to lighting, spatial logic, and world knowledge.

In parallel, Black Forest Labs continues to follow an open-core release strategy. The company provides hosted, performance-optimized versions of FLUX.2 for commercial deployments, while also publishing inspectable open-weight models that researchers and independent developers can run locally. This approach extends a track record begun with FLUX.1, which became the most widely used open image model globally.

Model Variants and Deployment Options

Flux.2 arrives with 5 variants as follows:

• Flux.2 [Pro]: This is the highest-performance tier, intended for applications that require minimal latency and maximal visual fidelity. It is available through the BFL Playground, the FLUX API, and partner platforms. The model aims to match leading closed-weight systems in prompt adherence and image quality while reducing compute demand.

• Flux.2 [Flex]: This version exposes parameters such as the number of sampling steps and the guidance scale. The design enables developers to tune the trade-offs between speed, text accuracy, and detail fidelity. In practice, this enables workflows where low-step previews can be generated quickly before higher-step renders are invoked.

• Flux.2 [Dev]: The most notable release for the open ecosystem is the 32-billion-parameter open-weight checkpoint which integrates text-to-image generation and image editing into a single model. It supports multi-reference conditioning without requiring separate modules or pipelines. The model can run locally using BFL’s reference inference code or optimized fp8 implementations developed in partnership with NVIDIA and ComfyUI. Hosted inference is also available via FAL, Replicate, Runware, Verda, TogetherAI, Cloudflare, and DeepInfra.

• Flux.2 [Klein]: Coming soon, this size-distilled model is released under Apache 2.0 and is intended to offer improved performance relative to comparable models of the same size trained from scratch. A beta program is currently open.

• Flux.2 – VAE: Released under the enterprise friendly (even for commercial use) Apache 2.0 license, updated variational autoencoder provides the latent space that underpins all Flux.2 variants. The VAE emphasizes an optimized balance between reconstruction fidelity, learnability, and compression rate—a long-standing challenge for latent-space generative architectures.

Benchmark Performance

Black Forest Labs published two sets of evaluations highlighting FLUX.2’s performance relative to other open-weight and hosted image-generation models. In head-to-head win-rate comparisons across three categories—text-to-image generation, single-reference editing, and multi-reference editing—FLUX.2 [Dev] led all open-weight alternatives by a substantial margin.

It achieved a 66.6% win rate in text-to-image generation (vs. 51.3% for Qwen-Image and 48.1% for Hunyuan Image 3.0), 59.8% in single-reference editing (vs. 49.3% for Qwen-Image and 41.2% for FLUX.1 Kontext), and 63.6% in multi-reference editing (vs. 36.4% for Qwen-Image). These results reflect consistent gains over both earlier FLUX.1 models and contemporary open-weight systems.

A second benchmark compared model quality using ELO scores against approximate per-image cost. In this analysis, FLUX.2 [Pro], FLUX.2 [Flex], and FLUX.2 [Dev] cluster in the upper-quality, lower-cost region of the chart, with ELO scores in the ~1030–1050 band while operating in the 2–6 cent range.

By contrast, earlier models such as FLUX.1 Kontext [max] and Hunyuan Image 3.0 appear significantly lower on the ELO axis despite similar or higher per-image costs. Only proprietary competitors like Nano Banana 2 reach higher ELO levels, but at noticeably elevated cost. According to BFL, this po…

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🔗 Sumber: venturebeat.com