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.

NVIDIA's Llama Nemotron Nano VL Sets New Standard in OCR Accuracy and Document Intelligence

 NVIDIA has unveiled its latest advancement in artificial intelligence: the Llama Nemotron Nano Vision-Language (VL) model, a cutting-edge solution designed to transform intelligent document processing. This compact yet powerful model has achieved top accuracy on the OCRBench v2 benchmark, setting a new standard for optical character recognition (OCR) and document understanding tasks.

Revolutionizing Document Intelligence

The Llama Nemotron Nano VL model is engineered to handle complex, multimodal documents such as PDFs, graphs, charts, tables, diagrams, and dashboards. Its capabilities extend to:

• Question Answering (Q/A): Accurately responding to queries based on document content.

• Text and Table Processing: Extracting and interpreting textual data and tabular information.

• Chart and Graph Parsing: Understanding and analyzing visual data representations.

• Infographic and Diagram Interpretation: Deciphering complex visual elements to extract meaningful insights.

By integrating advanced multi-modal capabilities, the model ensures that enterprises can swiftly surface critical information from their business documents, enhancing decision-making processes.

Benchmarking Excellence with OCRBench v2

The model's prowess is validated through rigorous testing on OCRBench v2, a comprehensive benchmark that evaluates OCR and document understanding across diverse real-world scenarios. OCRBench v2 encompasses documents commonly found in finance, healthcare, legal, and government sectors, including invoices, receipts, and contracts.

Key highlights of the benchmark include:

• Eight Text-Reading Capabilities: Assessing various aspects of text recognition and understanding.

• 10,000 Human-Verified Q&A Pairs: Providing a nuanced assessment of model performance.

• 31 Real-World Scenarios: Ensuring models can handle the complexities of enterprise document processing workflows.

The Llama Nemotron Nano VL model's exceptional performance in this benchmark underscores its ability to handle tasks like text spotting, element parsing, and table extraction with unparalleled accuracy.

Innovative Architecture and Training

Several key factors contribute to the model's industry-leading performance:

• Customization of Llama-3.1 8B: Tailoring the base model to enhance document understanding capabilities.

• Integration of NeMo Retriever Parse Data: Leveraging high-quality data for improved text and table parsing.

• Incorporation of C-RADIO Vision Transformer: Enhancing the model's ability to parse text and extract insights from complex visual layouts.

These innovations enable the Llama Nemotron Nano VL model to deliver high performance in intelligent document processing, making it a powerful tool for enterprises aiming to automate and scale their document analysis operations.

Accessible and Efficient Deployment

Designed with efficiency in mind, the model allows enterprises to deploy sophisticated document understanding systems without incurring high infrastructure costs. It is available as an NVIDIA NIM API and can be downloaded from Hugging Face, facilitating seamless integration into existing workflows.

Conclusion

NVIDIA's Llama Nemotron Nano VL model represents a significant leap forward in the field of intelligent document processing. By achieving top accuracy on OCRBench v2 and offering a suite of advanced capabilities, it empowers enterprises to extract valuable insights from complex documents efficiently and accurately. As organizations continue to seek automation in document analysis, this model stands out as a leading solution in the AI landscape.

NVIDIA Introduces AceReason-Nemotron: Enhancing Math and Code Reasoning through Reinforcement Learning

 NVIDIA has unveiled AceReason-Nemotron, a 14-billion-parameter open-source model designed to enhance mathematical and coding reasoning through large-scale reinforcement learning (RL). This model demonstrates that RL can significantly improve reasoning capabilities in small to mid-sized models, surpassing traditional distillation-based approaches.

Key Features and Innovations

• Sequential RL Training Strategy: The model undergoes a two-phase RL training process—initially on math-only prompts, followed by code-only prompts. This approach not only boosts performance in respective domains but also ensures minimal degradation across tasks. 

• Enhanced Benchmark Performance: AceReason-Nemotron-14B achieves notable improvements on various benchmarks:

  • AIME 2025: 67.4% (+17.4%)

  • LiveCodeBench v5: 61.1% (+8%)

  • LiveCodeBench v6: 54.9% (+7%) 

• Robust Data Curation Pipeline: NVIDIA developed a comprehensive data curation system to collect challenging prompts with verifiable answers, facilitating effective verification-based RL across both math and code domains. 

• Curriculum Learning and Stability: The training incorporates curriculum learning with progressively increasing response lengths and utilizes on-policy parameter updates to stabilize the RL process. 

Implications for AI Development

AceReason-Nemotron's success illustrates the potential of reinforcement learning in enhancing the reasoning abilities of AI models, particularly in mathematical and coding tasks. By releasing this model under the NVIDIA Open Model License, NVIDIA encourages further research and development in the AI community.

NVIDIA Unveils Llama Nemotron Nano 4B: A Compact, High-Performance Open Reasoning Model for Edge AI and Scientific Applications

 NVIDIA has introduced Llama Nemotron Nano 4B, a 4.3 billion parameter open-source reasoning model designed to deliver high accuracy and efficiency across various tasks, including scientific computing, programming, symbolic mathematics, function execution, and instruction following. This compact model is tailored for edge deployment, making it ideal for applications requiring local processing with limited computational resources.

Key Features

• Enhanced Performance: Achieves up to 50% higher inference throughput compared to other leading open models with up to 8 billion parameters, ensuring faster and more efficient processing. 

• Hybrid Reasoning Capabilities: Supports both symbolic and neural reasoning, enabling the model to handle complex tasks that require a combination of logical deduction and pattern recognition.

• Edge Deployment Optimization: Specifically optimized for deployment on NVIDIA Jetson and RTX GPUs, allowing for secure, low-cost, and flexible AI inference at the edge. 

• Extended Context Handling: Capable of processing inputs with up to 128K context length, facilitating the handling of extensive and detailed information.

• Open Source Accessibility: Released under the NVIDIA Open Model License, the model is available for download and use via Hugging Face, promoting transparency and collaboration within the AI community.

Deployment and Use Cases

The Llama Nemotron Nano 4B model is particularly suited for:

• Scientific Research: Performing complex calculations and simulations in fields like physics, chemistry, and biology.

• Edge Computing: Enabling intelligent processing on devices with limited computational power, such as IoT devices and autonomous systems.

• Educational Tools: Assisting in teaching and learning environments that require interactive and responsive AI systems.

• Enterprise Applications: Integrating into business processes that demand efficient and accurate data analysis and decision-making support.

With its balance of compact size, high performance, and open accessibility, Llama Nemotron Nano 4B stands out as a versatile tool for advancing AI applications across various domains.

NVIDIA Launches Cosmos-Reason1: Pioneering AI Models for Physical Common Sense and Embodied Reasoning

 NVIDIA has unveiled Cosmos-Reason1, a groundbreaking suite of AI models aimed at advancing physical common sense and embodied reasoning in real-world environments. This release marks a significant step towards developing AI systems capable of understanding and interacting with the physical world in a human-like manner.

Understanding Cosmos-Reason1

Cosmos-Reason1 comprises multimodal large language models (LLMs) trained to interpret and reason about physical environments. These models are designed to process both textual and visual data, enabling them to make informed decisions based on real-world contexts. By integrating physical common sense and embodied reasoning, Cosmos-Reason1 aims to bridge the gap between AI and human-like understanding of the physical world. 

Key Features

• Multimodal Processing: Cosmos-Reason1 models can analyze and interpret both language and visual inputs, allowing for a comprehensive understanding of complex environments.

• Physical Common Sense Ontology: The models are built upon a hierarchical ontology that encapsulates knowledge about space, time, and fundamental physics, providing a structured framework for physical reasoning. 

• Embodied Reasoning Capabilities: Cosmos-Reason1 is equipped to simulate and predict physical interactions, enabling AI to perform tasks that require an understanding of cause and effect in the physical world.

• Benchmarking and Evaluation: NVIDIA has developed comprehensive benchmarks to assess the models' performance in physical common sense and embodied reasoning tasks, ensuring their reliability and effectiveness. 

Applications and Impact

The introduction of Cosmos-Reason1 holds significant implications for various industries:

• Robotics: Enhancing robots' ability to navigate and interact with dynamic environments. 

• Autonomous Vehicles: Improving decision-making processes in self-driving cars by providing a better understanding of physical surroundings.

• Healthcare: Assisting in the development of AI systems that can comprehend and respond to physical cues in medical settings.

• Manufacturing: Optimizing automation processes by enabling machines to adapt to changes in physical environments.

Access and Licensing

NVIDIA has made Cosmos-Reason1 available under the NVIDIA Open Model License, promoting transparency and collaboration within the AI community. Developers and researchers can access the models and related resources through the following platforms:

• GitHub Repository: Cosmos-Reason1 on GitHub

• Hugging Face Model Page: Cosmos-Reason1-7B on Hugging Face

• NVIDIA Developer Portal: NVIDIA Cosmos for Developers

NVIDIA Unveils Parakeet-TDT-0.6B-v2: A Breakthrough in Open-Source Speech Recognition

 On May 1, 2025, NVIDIA released Parakeet-TDT-0.6B-v2, a state-of-the-art automatic speech recognition (ASR) model, now available on Hugging Face. This open-source model is designed to deliver high-speed, accurate transcriptions, setting a new benchmark in the field of speech-to-text technology.

Exceptional Performance and Speed

Parakeet-TDT-0.6B-v2 boasts 600 million parameters and utilizes a combination of the FastConformer encoder and TDT decoder architectures. When deployed on NVIDIA's GPU-accelerated hardware, the model can transcribe 60 minutes of audio in just one second, achieving a Real-Time Factor (RTFx) of 3386.02 with a batch size of 128. This performance places it at the top of current ASR benchmarks maintained by Hugging Face. 

Comprehensive Feature Set

The model supports:

• Punctuation and Capitalization: Enhances readability of transcriptions.

• Word-Level Timestamping: Facilitates precise alignment between audio and text.

• Robustness to Noise: Maintains accuracy even in varied noise conditions and telephony-style audio formats.

These features make it suitable for applications such as transcription services, voice assistants, subtitle generation, and conversational AI platforms. 

Training Data and Methodology

Parakeet-TDT-0.6B-v2 was trained on the Granary dataset, comprising approximately 120,000 hours of English audio. This includes 10,000 hours of high-quality human-transcribed data and 110,000 hours of pseudo-labeled speech from sources like LibriSpeech, Mozilla Common Voice, YouTube-Commons, and Librilight. NVIDIA plans to make the Granary dataset publicly available following its presentation at Interspeech 2025. 

Accessibility and Deployment

Developers can deploy the model using NVIDIA’s NeMo toolkit, compatible with Python and PyTorch. The model is released under the Creative Commons CC-BY-4.0 license, permitting both commercial and non-commercial use. It is optimized for NVIDIA GPU environments, including A100, H100, T4, and V100 boards, but can also run on systems with as little as 2GB of RAM. 

Implications for the AI Community

The release of Parakeet-TDT-0.6B-v2 underscores NVIDIA's commitment to advancing open-source AI tools. By providing a high-performance, accessible ASR model, NVIDIA empowers developers, researchers, and enterprises to integrate cutting-edge speech recognition capabilities into their applications, fostering innovation across various industries.