---
library_name: transformers
license: other
license_name: nvidia-open-model-license
license_link: >-
https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-generation
language:
- en
tags:
- nvidia
- nemotron-cascade
- reasoning
- general-purpose
- SFT
- RL
- pytorch
---
# Nemotron-Cascade-14B-Thinking
[](PAPER_LINK)
[](NOT_YET_READY)
[](NOT_YET_READY)
[](https://huggingface.co/collections/nvidia/nemotron-cascade)
## Introduction
We're excited to introduce [Nemotron-Cascade-14B-Thinking](https://huggingface.co/nvidia/Nemotron-Cascade-14B-Thinking), a powerful general-purpose model trained through sequential and domain-wise reinforcement learning. Nemotron-Cascade-14B-Thinking is post-trained from the [Qwen3-14B Base](https://huggingface.co/Qwen/Qwen3-14B-Base) model, and it achieves best-in-class performance across a wide range of benchmarks. Different from [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B), Nemotron-Cascade-14B-Thinking is designed exclusively for the ***thinking*** mode.
## Training Pipeline
The training pipeline for Nemotron-Cascade begins with a multi-stage SFT phase to equip the model with foundational skills. Subsequently, Cascade RL is applied across multiple domains to further enhance the model’s performance in these areas.
## Results
- We evaluate our model against competitive reasoning models on a diverse set of benchmarks, covering general-knowledge reasoning, alignment and instruction following, mathematical reasoning, competitive programming, software engineering, and tool-use proficiency.
- For Nemotron-Cascade models, we use a maximum generation length of 64K tokens and set the temperature to 0.6 and top-p to 0.95 for reasoning tasks.
- Our Nemotron-Cascade-14B-Thinking achieves best-in-class performance across almost all benchmarks. Remarkably, Nemotron-Cascade-14B-Thinking surpasses DeepSeek-R1-0528 (671B) by a clear margin across all LCB v5, v6, and Pro benchmarks.
| **Benchmark
Metric: Pass@1** | **Qwen3-14B** | **DeepSeek-R1-0528 671B** | **Gemini-2.5-Flash-Thinking** | **Nemotron-Cascade-14B-Thinking** |
| :---- | :---: | :---: | :---: | :---: |
| ***Knowledge Reasoning*** |
| MMLU | 84.9 | 89.9 | - | 85.1 |
| MMLU Pro | 77.6 | 85.0 | 81.9 | 77.0 |
| GPQA-Diamond | 64.0 | 81.0 | 82.8 | 69.6 |
| ***Alignment*** |
| ArenaHard | 91.7 | 95.1 | 95.7 | 89.5 |
| IFEval (Strict Prompt) | 85.4 | 84.1 | 89.8 | 81.9 |
| IFBench | 33.7 | 38.0 | 36.1 | 41.7 |
| ***Math*** |
| AIME 2024 | 79.3 | 91.4 | 82.3 | 89.7 |
| AIME 2025 | 70.4 | 87.5 | 72.0 | 83.3 |
| ***Code*** |
| LCB v5 (08/24-02/25) | 65.2 | 74.8 | 63.4 | **77.5** |
| LCB v6 (08/24-05/25) | 63.5 | 73.3 | 61.9 | **74.6** |
| LCB Pro 25Q2 (Easy) | 53.6 | 63.9 | 47.4 | **68.9** |
| LCB Pro 25Q2 (Med) | 2.6 | 7.0 | 1.8 | **10.5** |
| SWE Verified (Agentless) | 27.4 | 57.6 | 48.9 | 43.1 |
| ***Tool Calling*** |
| BFCL V3 | 70.4 | 67.9 | 68.6 | 67.5 |
## Evaluation Tookit
To reproduce our results, please check evaluation code, scripts, cached prediction files in https://huggingface.co/nvidia/Nemotron-Cascade-14B-Thinking/blob/main/evaluation/README.md
## Chat Template
Nemotron-Cascade-14B-Thinking follows the Qwen3-style ChatML template and is designed exclusively for the ***thinking*** mode. To align with the template used in [Nemotron-Cascade-8B](https://huggingface.co/nvidia/Nemotron-Cascade-8B), the `" /think"` tag should be appended to the end of the user input. Note that a leading space is included in this tag to ensure correct tokenization.
To reduce the context length in a multi-turn conversation, we include only the final summary of the model’s output in the conversation history and change the user turn’s `" /think"` tag to `" /no_think"`.
A brief example is shown below:
```python
from transformers import AutoTokenizer
model_name = 'nvidia/Nemotron-Cascade-14B-Thinking'
tokenizer = AutoTokenizer.from_pretrained(model_name)
'''
single-turn example
'''
messages = [
{"role": "user", "content": "calculate 1+1?"}
]
# only thinking mode is supported (enable_thinking=True)
prompt_thinking = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
# prompt_thinking = '<|im_start|>system\nYou are a helpful and harmless assistant.<|im_end|>\n<|im_start|>user\ncalculate 1+1? /think<|im_end|>\n<|im_start|>assistant\n'
'''
multi-turn example
'''
messages = [
{"role": "user", "content": "calculate 1+1?"},
{"role": "assistant", "content": "THINKING_CONTENT\nTo calculate \\(1 + 1\\):\n\n1. **Identify the operation**: This is a basic addition problem involving two integers.\n2. **Perform the addition**: \n \\(1 + 1 = 2\\).\n\n**Result**: \\(\\boxed{2}\\)",},
{"role": "user", "content": "what about 2+2"}
]
# only thinking mode is supported (enable_thinking=True)
prompt_thinking = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True)
# prompt_thinking = '<|im_start|>system\nYou are a helpful and harmless assistant.<|im_end|>\n<|im_start|>user\ncalculate 1+1? /no_think<|im_end|>\n<|im_start|>assistant\nTo calculate \\(1 + 1\\):\n\n1. **Identify the operation**: This is a basic addition problem involving two integers.\n2. **Perform the addition**: \n \\(1 + 1 = 2\\).\n\n**Result**: \\(\\boxed{2}\\)<|im_end|>\n<|im_start|>user\nwhat about 2+2 /think<|im_end|>\n<|im_start|>assistant\n'
```
## Release Date
Dec 08, 2025
## License
Your use of this model is governed by the [NVIDIA Open Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).
## Citation
```
@article{Cascade_RL_Scaling_Cascaded_Reinforcement_Learning,
title={Cascade RL: Scaling Cascaded Reinforcement Learning for General-Purpose Reasoning Models},
author={Wang, Boxin and Lee, Chankyu and Lee, Nayeon and Lin, Sheng-Chieh and Dai, Wenliang and Chen, Yang and Chen, Yangyi and Yang, Zhuolin and Liu, Zihan and Shoeybi, Mohammad and Catanzaro, Bryan and Ping, Wei},
year={2025}
}
```