Model Overview

DLER-R1-1.5B

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

Description:

DLER-Qwen-R1-1.5B is an ultra-efficient 1.5B 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 1.5B model, DLER-Qwen-R1-1.5B 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
Deepseek-R1-1.5B	84.31	5500	29.79	16916	61.97	10967	38.41	7494	44.07	11620	10499
DLER-R1-1.5B	86.95 (+2.64%)	1652 (-70%)	34.375 (+4.59%)	3551 (-80%)	70.48 (+8.51%)	2537 (-77%)	43.58 (+5.18%)	2029 (-73%)	48.314 (+4.24%)	2563 (-78%)	2466 (-77%)

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-R1-1.5B').to(device)
tokenizer = AutoTokenizer.from_pretrained('nvidia/DLER-R1-1.5B')


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:

@misc{liu2025dlerdoinglengthpenalty,
      title={DLER: Doing Length pEnalty Right - Incentivizing More Intelligence per Token via Reinforcement Learning}, 
      author={Shih-Yang Liu and Xin Dong and Ximing Lu and Shizhe Diao and Mingjie Liu and Min-Hung Chen and Hongxu Yin and Yu-Chiang Frank Wang and Kwang-Ting Cheng and Yejin Choi and Jan Kautz and Pavlo Molchanov},
      year={2025},
      eprint={2510.15110},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.15110}, 
}