NVIDIA's ProRL: Advancing Reasoning in Language Models Through Prolonged Reinforcement Learning

 NVIDIA has unveiled ProRL (Prolonged Reinforcement Learning), a groundbreaking training methodology designed to expand the reasoning boundaries of large language models (LLMs). By extending the duration and stability of reinforcement learning (RL) training, ProRL enables LLMs to develop novel reasoning strategies that surpass the capabilities of their base models.

Understanding ProRL

Traditional RL approaches often face challenges in enhancing the reasoning abilities of LLMs, sometimes merely amplifying existing patterns without fostering genuine innovation. ProRL addresses this by introducing:

• KL Divergence Control: Maintains a balance between exploring new strategies and retaining learned knowledge.

• Reference Policy Resetting: Periodically resets the policy to prevent convergence on suboptimal solutions.

• Diverse Task Suite: Engages models in a wide array of tasks to promote generalization and adaptability.

These components collectively ensure that models not only learn more effectively but also develop unique reasoning pathways previously inaccessible through standard training methods.

Key Findings

Empirical evaluations demonstrate that ProRL-trained models consistently outperform their base counterparts across various benchmarks, including scenarios where base models fail entirely. Notably, improvements were observed in:

• Pass@k Evaluations: Higher success rates in generating correct outputs within k attempts.

• Creativity Index: Enhanced ability to produce novel solutions not present in the training data.

These results indicate that prolonged RL training can lead to the emergence of new reasoning capabilities, expanding the solution space beyond initial limitations.

Implications for AI Development

The introduction of ProRL signifies a pivotal shift in AI training paradigms. By demonstrating that extended and stable RL training can foster genuine reasoning advancements, NVIDIA paves the way for more sophisticated and adaptable AI systems. This has profound implications for applications requiring complex decision-making and problem-solving abilities.

Accessing ProRL Resources

To facilitate further research and development, NVIDIA has released the model weights associated with ProRL. Interested parties can access these resources here:

• ProRL Research Paper

• Model Weights on Hugging Face

These resources provide valuable insights and tools for researchers aiming to explore the frontiers of AI reasoning capabilities.

Ultra-FineWeb: A Trillion-Token Dataset Enhancing LLM Accuracy Across Benchmarks

 Researchers from Tsinghua University and ModelBest have introduced Ultra-FineWeb, a large-scale, high-quality dataset comprising approximately 1 trillion English tokens and 120 billion Chinese tokens. This dataset aims to enhance the performance of large language models (LLMs) by providing cleaner and more efficient training data.

Efficient Data Filtering Pipeline

The creation of Ultra-FineWeb involved an efficient data filtering pipeline that addresses two main challenges in data preparation for LLMs:

1. Lack of Efficient Data Verification Strategy:
   Traditional methods struggle to provide timely feedback on data quality. To overcome this, the researchers introduced a computationally efficient verification strategy that enables rapid evaluation of data impact on LLM training with minimal computational cost.

2. Selection of Seed Data for Classifier Training:
   Selecting appropriate seed data often relies heavily on human expertise, introducing subjectivity. The team optimized the selection process by integrating the verification strategy, improving filtering efficiency and classifier robustness.

A lightweight classifier based on fastText was employed to efficiently filter high-quality data, significantly reducing inference costs compared to LLM-based classifiers.

Benchmark Performance

Empirical results demonstrate that LLMs trained on Ultra-FineWeb exhibit significant performance improvements across multiple benchmark tasks, including MMLU, ARC, CommonSenseQA, and others. The dataset's quality contributes to enhanced training efficiency and model accuracy.

Availability

Ultra-FineWeb is available on Hugging Face, providing researchers and developers with access to this extensive dataset for training and evaluating LLMs.

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References

1. Researchers from Tsinghua and ModelBest Release Ultra-FineWeb: A Trillion-Token Dataset Enhancing LLM Accuracy Across Benchmarks – MarkTechPost. 

2. Ultra-FineWeb Dataset on Hugging Face. 

3. Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data

ByteDance Launches Seed1.5-VL: A Compact Yet Powerful Vision-Language Model for Multimodal AI

 In a significant stride towards advancing multimodal artificial intelligence, ByteDance has unveiled Seed1.5-VL, a vision-language foundation model designed to excel in general-purpose understanding and reasoning tasks across various modalities. Despite its relatively compact architecture, Seed1.5-VL delivers state-of-the-art performance on a wide array of benchmarks, positioning itself as a formidable contender in the AI landscape.

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Model Architecture and Design

Seed1.5-VL is composed of a 532 million-parameter vision encoder coupled with a 20 billion-parameter Mixture-of-Experts (MoE) large language model. This design enables the model to process and integrate information from both visual and textual inputs efficiently. The MoE architecture allows for activating only a subset of the model's parameters during inference, optimizing computational resources without compromising performance. 

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Benchmark Performance

The model has demonstrated exceptional capabilities, achieving state-of-the-art results on 38 out of 60 public vision-language benchmarks. Notably, Seed1.5-VL excels in tasks such as:

• Visual Question Answering (VQA): Providing accurate answers to questions based on visual content.

• Optical Character Recognition (OCR): Accurately reading and interpreting text within images.

• Diagram and Chart Understanding: Interpreting complex visual data representations.

• Visual Grounding: Associating textual descriptions with corresponding regions in images.

• 3D Spatial Understanding: Comprehending three-dimensional spatial relationships in visual inputs.

• Video Comprehension: Analyzing and understanding temporal sequences in video data.

These capabilities underscore the model's versatility and robustness across diverse multimodal tasks.arXiv

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Agent-Centric Abilities

Beyond traditional vision-language tasks, Seed1.5-VL exhibits advanced agent-centric abilities. It demonstrates strong performance in interactive tasks such as GUI control and gameplay, showcasing its potential in applications requiring real-time decision-making and interaction. 

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Efficiency and Practical Applications

One of the standout features of Seed1.5-VL is its efficiency. By leveraging the MoE architecture, the model maintains high performance while reducing computational overhead. This efficiency makes it suitable for deployment in real-world applications, including:Surveillance Analysis: Interpreting and analyzing video feeds for security purposes.

• User Interface Automation: Controlling and interacting with graphical user interfaces.

• Educational Tools: Assisting in learning environments through multimodal content understanding.

The model's ability to handle complex reasoning and diverse input types positions it as a valuable asset across various industries.

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Accessibility and Open-Source Commitment

ByteDance has made Seed1.5-VL accessible to the broader AI community. The model is available for testing via the Volcano Engine API and has been open-sourced on platforms like GitHub and Hugging Face. This commitment to openness fosters collaboration and accelerates advancements in multimodal AI research.

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Conclusion

Seed1.5-VL represents a significant advancement in the field of multimodal AI, combining efficiency with high performance across a range of complex tasks. Its compact architecture, coupled with state-of-the-art results, makes it a compelling choice for researchers and practitioners seeking versatile and powerful AI solutions.

For more information and to explore the model further, visit the official GitHub repository and the technical report on arXiv.

AlphaEvolve: How DeepMind’s Gemini-Powered Agent Is Reinventing Algorithm Design

 As artificial intelligence becomes more deeply integrated into the way we build software, DeepMind is once again leading the charge—with a new agent that doesn’t just write code, but evolves it. Introducing AlphaEvolve, an AI coding agent powered by Gemini 2.0 Pro and Gemini 2.0 Flash models, designed to autonomously discover, test, and refine algorithms.

Unlike typical AI code tools, AlphaEvolve combines the reasoning power of large language models (LLMs) with the adaptability of evolutionary computation. The result? An agent that can produce high-performance algorithmic solutions—and in some cases, outperform those written by top human experts.

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What Is AlphaEvolve?

AlphaEvolve is a self-improving coding agent that leverages the capabilities of Gemini 2.0 models to solve algorithmic problems in a way that mimics natural selection. This isn’t prompt-in, code-out. Instead, it’s a dynamic system where the agent proposes code candidates, evaluates them, improves upon them, and repeats the process through thousands of iterations.

These aren’t just AI guesses. The candidates are rigorously benchmarked and evolved using performance feedback—selecting the best performers and mutating them to discover even better versions over time.

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How It Works: Evolution + LLMs

At the core of AlphaEvolve is an elegant idea: combine evolutionary search with LLM-driven reasoning.

1. Initial Code Generation: Gemini 2.0 Pro and Flash models generate a pool of candidate algorithms based on a given problem.

2. Evaluation Loop: These programs are tested using problem-specific benchmarks—such as how well they sort, pack, or schedule items.

3. Evolution: The best-performing algorithms are "bred" through mutation and recombination. The LLMs guide this evolution by proposing tweaks and structural improvements.

4. Iteration: This process continues across generations, yielding progressively better-performing solutions.

It’s a system that improves with experience—just like evolution in nature, only massively accelerated by compute and code.

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Beating the Benchmarks

DeepMind tested AlphaEvolve on a range of classic algorithmic problems, including:

• Sorting algorithms

• Bin packing

• Job scheduling

• The Traveling Salesperson Problem (TSP)

These problems are fundamental to computer science and are often featured in coding interviews and high-performance systems.

In multiple benchmarks, AlphaEvolve generated algorithms that matched or outperformed human-designed solutions, especially in runtime efficiency and generalizability across input sizes. In some cases, it even discovered novel solutions—new algorithmic strategies that had not previously been documented in the academic literature.

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Powered by Gemini 2.0 Pro and Flash

AlphaEvolve’s breakthroughs are driven by Gemini 2.0 Flash and Gemini 2.0 Pro, part of Google DeepMind’s family of cutting-edge LLMs.

• Gemini 2.0 Flash is optimized for fast and cost-efficient tasks like initial code generation and mutation.

• Gemini 2.0 Pro is used for deeper evaluations, higher reasoning tasks, and more complex synthesis.

This dual-model approach allows AlphaEvolve to balance scale, speed, and intelligence—delivering an agent that can generate thousands of variants and intelligently select which ones to evolve further.

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A Glimpse into AI-Augmented Programming

What makes AlphaEvolve more than just a research showcase is its implication for the future of software engineering.

With tools like AlphaEvolve, we are moving toward a future where:

• Developers define the goal and constraints.

• AI agents autonomously generate, test, and optimize code.

• Human coders curate and guide rather than implement everything manually.

This shift could lead to faster innovation cycles, more performant codebases, and democratized access to high-quality algorithms—even for developers without deep expertise in optimization theory.

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The Takeaway

DeepMind’s AlphaEvolve is a powerful example of what’s possible when evolutionary computing meets LLM reasoning. Powered by Gemini 2.0 Flash and Pro, it represents a new generation of AI agents that don’t just assist in programming—they design and evolve new algorithms on their own.

By outperforming traditional solutions in key problems, AlphaEvolve shows that AI isn’t just catching up to human capability—it’s starting to lead in areas of complex problem-solving and algorithm design.

As we look to the future, the question isn’t whether AI will write our code—but how much better that code could become when AI writes it with evolution in mind.

Sakana AI Unveils Continuous Thought Machines: A Leap Towards Human-like AI Reasoning

 Tokyo-based Sakana AI has introduced a novel AI architecture named Continuous Thought Machines (CTMs), aiming to enable artificial intelligence models to reason more like human brains and with significantly less explicit guidance. This development, announced on May 12, 2025, tackles a core challenge in AI: moving beyond pattern recognition to achieve genuine, step-by-step reasoning.

CTMs represent a departure from traditional deep learning models by explicitly incorporating time and the synchronization of neuron activity as a fundamental component of their reasoning process. This approach is inspired by the complex neural dynamics observed in biological brains, where the timing and interplay between neurons are critical to information processing.

Most current AI architectures, while powerful, abstract away these temporal dynamics. Sakana AI's CTMs, however, are designed to leverage these neural dynamics as their core representation.The architecture introduces two key innovations: neuron-level temporal processing, where individual neurons use unique parameters to process a history of incoming signals, and neural synchronization, which is employed as a latent representation for the model to observe data and make predictions.

This unique design allows CTMs to "think" through problems in a series of internal "thought steps," effectively creating an internal dimension where reasoning can unfold. This contrasts with conventional models that might process information in a single pass.The ability to observe this internal process also offers greater interpretability, allowing researchers to visualize how the model arrives at a solution, much like tracing a path through a maze.

Sakana AI's research indicates that CTMs demonstrate strong performance and versatility across a range of challenging tasks, including image classification, maze solving, sorting, and question-answering. A notable feature is their capacity for adaptive compute, meaning the model can dynamically adjust its computational effort, stopping earlier for simpler tasks or continuing to process for more complex challenges without needing additional complex instructions.

The introduction of Continuous Thought Machines marks a significant step in the quest for more biologically plausible and powerful AI systems.[2] By focusing on the temporal dynamics of neural activity, Sakana AI aims to bridge the gap between the computational efficiency of current AI and the nuanced reasoning capabilities of the human brain, potentially unlocking new frontiers in artificial intelligence.

Microsoft Launches Phi-4-Reasoning-Plus: Small Model, Big Reasoning Power

Microsoft has unveiled Phi-4-Reasoning-Plus, a compact yet highly capable open-weight language model built for deep, structured reasoning. With just 14 billion parameters, it punches far above its weight—outperforming much larger models on key benchmarks in logic, math, and science.

Phi-4-Reasoning-Plus is a refinement of Microsoft’s earlier Phi-4 model. It uses advanced supervised fine-tuning and reinforcement learning to deliver high reasoning accuracy in a lightweight format. Trained on 16 billion tokens—half of which are unique—the model’s data includes synthetic prompts, carefully filtered web content, and a dedicated reinforcement learning phase focused on solving 6,400 math problems.

What makes this model especially valuable to developers and businesses is its MIT open-source license, allowing free use, modification, and commercial deployment. It's also designed to run efficiently on common AI frameworks like Hugging Face Transformers, vLLM, llama.cpp, and Ollama—making it easy to integrate across platforms.

Key Features of Phi-4-Reasoning-Plus:

• ✅ 14B parameters with performance rivaling 70B+ models in reasoning tasks

• ✅ Outperforms larger LLMs in math, coding, and logical reasoning

• ✅ Uses special tokens to improve transparency in reasoning steps

• ✅ Trained with outcome-based reinforcement learning for better accuracy and brevity

• ✅ Released under the MIT license for open commercial use

• ✅ Compatible with lightweight inference frameworks

One of the standout results? Phi-4-Reasoning-Plus achieved a higher first-pass score on the AIME 2025 math exam than a 70B model—an impressive feat that showcases its reasoning efficiency despite a smaller model size.

Takeaway

Microsoft’s Phi-4-Reasoning-Plus marks a turning point in AI development: high performance no longer depends on massive scale. This small but mighty model proves that with smarter training and tuning, compact LLMs can rival giants in performance—while being easier to deploy, more cost-effective, and openly available. It’s a big leap forward for accessible AI, especially for startups, educators, researchers, and businesses that need powerful reasoning without the heavy compute demands.