By applying SFT and GRPO on difficult math problems, we enhanced the performance of DeepSeek-R1-Distill-Qwen-14B and developed Fast-Math-R1-14B, which achieves approx. 30% faster inference on average, while maintaining accuracy.

In addition, we trained and open-sourced Fast-OpenMath-Nemotron-14B, an efficiency-optimized version of NVIDIA’s OpenMath-Nemotron-14B, following the same approach. Compared to OpenMath-Nemotron-14B, this model enables approx. 30% faster inference on average, with minimal loss in performance.

Technical details can be found in our github repository. Project page: https://analokmaus.github.io/Fast-Math-R1/

Note: This model likely inherits the ability to perform inference in TIR mode from the original model. However, all of our experiments were conducted in CoT mode, and its performance in TIR mode has not been evaluated.

Evaluation

		AIME 2024		AIME 2025
Model	Token budget	Pass@1 (avg. 64)	Mean output tokens	Pass@1 (avg. 64)	Mean output tokens
OpenMath-Nemotron-14B	32000	76.2	11493	64.5	13414
	24000	75.4	11417	63.4	13046
	16000	66	10399	54.2	11422
	12000	55	9053	40	9609
	8000	36	6978	27.2	7083
Fast-OpenMath-Nemotron-14B	32000	70.7	9603	61.4	11424
	24000	70.6	9567	60.9	11271
	16000	66.6	8954	55.3	10190
	12000	59.4	7927	45.6	8752
	8000	47.6	6282	33.8	6589

Inference

vLLM

python

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer


model_path = 'RabotniKuma/Fast-OpenMath-Nemotron-14B'
vllm_engine = LLM(
    model=model_path,
    max_model_len=8192,
    gpu_memory_utilization=0.9,
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)


sampling_params = SamplingParams(
    temperature=1.0,
    top_p=0.90,
    min_p=0.05,
    max_tokens=8192,
    stop='</think>',  # For even faster inference, applying early stopping at the </think> tag and extracting the final boxed content is recommended.
)
messages = [
    {
        'role': 'user',
        'content': (
            'Solve the problem, and put the answer in \boxed{{}}. '
            'Sarah is twice as old as her youngest brother. If the difference between their ages is 15 years. How old is her youngest brother?'
        )
    }
]
messages = tokenizer.apply_chat_template(
    conversation=messages,
    tokenize=False,
    add_generation_prompt=True
)
response = vllm_engine.generate(messages, sampling_params=sampling_params)