NVIDIA AI introduced Open third place New Yorkeris a family of large language models (LLMS) designed to accomplish complex inference tasks excellently between mathematics, science and code. This model kit – should have 1.5b, 7b, 14b and 32b parameter versions– Already Refined from 671B DeepSeek R1 0528 modelcapturing its advanced reasoning capabilities in significantly smaller, more efficient models.
This release positiones NVIDIA as a major contributor to the open source LLM ecosystem, providing a model that drives the latest performance (SOTA) performance while remaining commercially allowed and widely accessible by embracing faces.
Model Overview and Architecture
✅Distillation from DeepSeek R1 0528 (671b)
The core of open planning New Yorkers is Distillation Strategy This passes inference capability from DeepSeek R1 (a huge 671b parameter model) to smaller architectures. This process takes precedence Reasoning summary On the original token prediction, a compact model can be effectively executed on structured, high-cognitive tasks.
Distillation data set emphasizes Mathematics, Sciences and Programming Languagesalign model functions with key inference domains.
📊Model variants and specifications
| Model name | parameter | Expected use | Embrace the face page |
|---|---|---|---|
| OpenReasoning-Nemotron-1.5b | 1.5b | Introductory reasoning and reasoning | Related |
| OpenReasoning-Nemotron-7b | 7b | Mesoscale reasoning, suitable for code/math | Related |
| OpenReasoning-Nemotron-14b | 14b | Advanced reasoning skills | Related |
| OpenReasoning-Nemotron-32B | 32B | Boundary model of logic-intensive tasks | Related |
All models are Transformer architecturesupport FP16/INT8 Quantificationand NVIDIA GPU and NEMO frame.
Performance Benchmark
These model settings New state-of-the-art pass @ 1 size score Cross multiple inference benchmarks:
| Model | GPQA | mmlu -pro | hle | livecodebench | Scicode | Aime24 | Aime25 | HMMT February 2025 |
| 1.5b | 31.6 | 47.5 | 5.5 | 28.6 | 2.2 | 55.5 | 45.6 | 31.5 |
| 7b | 61.1 | 71.9 | 8.3 | 63.3 | 16.2 | 84.7 | 78.2 | 63.5 |
| 14b | 71.6 | 77.5 | 10.1 | 67.8 | 23.5 | 87.8 | 82.0 | 71.2 |
| 32B | 73.1 | 80.0 | 11.9 | 70.2 | 28.5 | 89.2 | 84.0 | 73.8 |
All referenced scores are via Genselect via @1.
🔍Genselect (heavy-duty mode)
use Generative choices for 64 candidates (“Genselect”), performance improvements further, especially at 32B:
- 32B Achievements: AIME24 89.2→93.3, AIME25 84.0→90.0, HMMT 73.8→96.7, LiveCodeBench 70.2→75.3.
This suggests strong emergency reasoning performance on a large scale.
Training data and reasoning specialization
The training corpus is Distillation, a high-quality subset The key features of the DeepSeek R1 0528 dataset include:
- Well-planned inference data From mathematics, science and CS disciplines.
- Timely design fine adjustment Aim to strengthen the multi-step thought chain.
- emphasize Logical consistency, constrain satisfactionand Symbol reasoning.
This intentional planning ensures a close integration with real-world reasoning problems and is found in the academic and applied ML fields.
Openness and ecosystem integration
All four open new hybrid models are released under one Open and commercially permitted licenseswith model card, evaluation script and reasoning weight that can be available on the hug surface:
These models are designed to be inserted NVIDIA NEMO Frameworkand support Tensorrt-llm,,,,, onnxand Embrace the Facial Transformer Toolchain that facilitates rapid deployment in production and research environments.
Key Use Cases
- Mathematics Tutor and Theorem Solver
- Scientific quality inspection agent and medical reasoning system
- Code generation and debugging assistant
- Reply to multiple skip link questions after thinking
- Synthetic data generation of structured domains
in conclusion
NVIDIA’s open new hybrid model is a pragmatic open source road Scaling reasoning capability without boundary scale calculation cost. These models achieve a strong balance by distilling from 671B DeepSeek R1 and targeting the high-lever reasoning domain Accuracy, efficiency and accessibility.
For developers, researchers, and businesses working on logic-intensive AI applications, OpenReasoning-Nemotron provides a compelling basis from trade-offs often accompanied by proprietary or over-agenitarian models.
🔍Frequently Asked Questions (FAQs)
Q1. What benchmarks are supported?
GPQA, MMLU-PRO, HLE, LIVECODEBENCH, SCICODE, AIME 2024/25, HMMT February 2025 (by @1).
Q2. How much data is used?
Distillation library 5 million inference log examples Cross-domain, generated by DeepSeek -R1-0528.
Q3. Is reinforcement learning used?
No – The model is purely trained through SFT, which can retain efficiency while achieving future RL research.
Q4. Can I extend the reasoning with Genselect?
Yes. Significantly improve performance using Genselect -32B jumped from 73.8 to 96.7 on HMMT with 64 candidates.
Check Technical details. All credits for this study are to the researchers on the project.
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