Model Overview

DLER-R1-7B

🚀 The leading efficient reasoning model for cutting-edge research and development 🌟

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:

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}
}