| --- |
| datasets: |
| - agentica-org/DeepScaleR-Preview-Dataset |
| language: |
| - en |
| metrics: |
| - accuracy |
| base_model: |
| - deepseek-ai/DeepSeek-R1-Distill-Qwen-7B |
| --- |
| # Model Overview |
| <div align="center"> |
| <span style="font-family: default; font-size: 1.5em;">DLER-R1-7B</span> |
| <div> |
| 🚀 The leading efficient reasoning model for cutting-edge research and development 🌟 |
| </div> |
| </div> |
|
|
| [](https://www.arxiv.org/abs/2510.15110) |
| [](https://github.com/NVlabs/DLER) |
| [](https://huggingface.co/collections/nvidia/reasoning-efficiency-research) |
| [](https://nvlabs.github.io/DLER/) |
|  |
|
|
| ### Description: |
| 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. |
|
|
| This model is for research and development only. |
|
|
| ### Evaluation Results: |
| | Model | MATH | Length | AIME | Length | AMC | Length | Minerva |Length | Olympiad |Length | Total Avg Length | |
| |------------------|----------|------------|--------------------|------------------|--------------------|------------------|--------------------|------------------|--------------------|------------------|-----------------| |
| | Deepseek-R1-7B | 93.60 | 3999 | 55.40 | 13241 | 82.90 | 7461 | 49.79 | 5199 | 58.21 | 8837 | 7747 | |
| | **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%)** | |
|
|
| ### Environment Setup |
|
|
| ``` |
| pip install transformers==4.51.3 |
| ``` |
| # Inference: |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| |
| model = AutoModelForCausalLM.from_pretrained('nvidia/DLER-R1-7B-Research').to(device) |
| tokenizer = AutoTokenizer.from_pretrained('nvidia/DLER-R1-7B-Research') |
| |
| messages = [ |
| {"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{}."}, |
| ] |
| |
| |
| tokenized_chat = tokenizer.apply_chat_template( |
| messages, |
| tokenize=True, |
| add_generation_prompt=True, |
| return_tensors="pt" |
| ).to(model.device) |
| |
| outputs = model.generate( |
| tokenized_chat, |
| max_new_tokens=10000, |
| eos_token_id=tokenizer.eos_token_id |
| ) |
| |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| ### License/Terms of Use |
| NSCLv1 |
|
|
|
|
| ## Citation |
| If you find our model helpful, please cite the following [paper](): |
|
|
| ``` |
| @article{liu2025dler, |
| title={DLER: Doing Length pEnalty Right-Incentivizing More Intelligence per Token via Reinforcement Learning}, |
| 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}, |
| journal={arXiv preprint arXiv:2510.15110}, |
| year={2025} |
| } |
| ``` |
|
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|
