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--- |
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datasets: |
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- agentica-org/DeepScaleR-Preview-Dataset |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
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--- |
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# Model Overview |
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<div align="center"> |
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<span style="font-family: default; font-size: 1.5em;">DLER-R1-7B</span> |
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<div> |
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🚀 The leading efficient reasoning model for cutting-edge research and development 🌟 |
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</div> |
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</div> |
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[](https://www.arxiv.org/abs/2510.15110) |
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[](https://github.com/NVlabs/DLER) |
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[](https://huggingface.co/collections/nvidia/reasoning-efficiency-research) |
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[](https://nvlabs.github.io/DLER/) |
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 |
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### Description: |
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DLER-Qwen-R1-7B is an ultra-efficient 7B open-weight reasoning model designed for challenging tasks such as mathematics, programming, and scientific problem-solving. It is trained with the DLER algorithm on agentica-org/DeepScaleR-Preview-Dataset. Compared to DeepSeek’s 7B model, DLER-Qwen-R1-7B achieves substantial efficiency gains, reducing the average response length by nearly 80% across diverse mathematical benchmarks with better accuracy. |
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This model is for research and development only. |
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### Evaluation Results: |
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| Model | MATH | Length | AIME | Length | AMC | Length | Minerva |Length | Olympiad |Length | Total Avg Length | |
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|------------------|----------|------------|--------------------|------------------|--------------------|------------------|--------------------|------------------|--------------------|------------------|-----------------| |
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| Deepseek-R1-7B | 93.60 | 3999 | 55.40 | 13241 | 82.90 | 7461 | 49.79 | 5199 | 58.21 | 8837 | 7747 | |
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| **DLER-R1-7B** | **94.21 (+0.61%)** | **1634 (-60%)** | **55.62 (+0.22%)** | **3230 (-76%)** | **84.41 (+1.51%)** | **2512 (-0.67%)** | **53.88 (+4.09%)** | **2058 (-61%)** | **60.48 (+2.27%)** | **2592 (-71%)** | **2405 (-69%)** | |
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### Environment Setup |
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``` |
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pip install transformers==4.51.3 |
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``` |
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# Inference: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = AutoModelForCausalLM.from_pretrained('nvidia/DLER-R1-7B-Research').to(device) |
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tokenizer = AutoTokenizer.from_pretrained('nvidia/DLER-R1-7B-Research') |
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messages = [ |
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{"role": "user", "content": "Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$"+" Let's think step by step and output the final answer within \\boxed{}."}, |
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] |
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tokenized_chat = tokenizer.apply_chat_template( |
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messages, |
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tokenize=True, |
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add_generation_prompt=True, |
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return_tensors="pt" |
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).to(model.device) |
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outputs = model.generate( |
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tokenized_chat, |
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max_new_tokens=10000, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
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### License/Terms of Use |
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NSCLv1 |
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## Citation |
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If you find our model helpful, please cite the following [paper](): |
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``` |
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@article{liu2025dler, |
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title={DLER: Doing Length pEnalty Right-Incentivizing More Intelligence per Token via Reinforcement Learning}, |
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author={Liu, Shih-Yang and Dong, Xin and Lu, Ximing and Diao, Shizhe and Liu, Mingjie and Chen, Min-Hung and Yin, Hongxu and Wang, Yu-Chiang Frank and Cheng, Kwang-Ting and Choi, Yejin and others}, |
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journal={arXiv preprint arXiv:2510.15110}, |
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year={2025} |
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} |
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``` |
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