Revolutionary template-augmented reasoning paradigm enpowers a 32B model to outperform o1-mini and DeepSeek-R1 distilled models in reasoning tasks.
We present the evaluation results of our ReasonFlux-F1-32B on challenging reasoning tasks including AIME2024,AIM2025,MATH500 and GPQA-Diamond. To make a fair comparison, we report the results of the LLMs on our evaluation scripts in
ReasonFlux-F1.
1from vllm import LLM, SamplingParams
2from transformers import AutoTokenizer
3
4model_id = 'Gen-Verse/ReasonFlux-F1-14B'
5
6model = LLM(
7 model_id,
8 tensor_parallel_size=8,
9)
10tokenizer = AutoTokenizer.from_pretrained(model_id)
11
12sampling_params = SamplingParams(
13 max_tokens=32768,
14)
15# 2022 AIME I Problems/Problem 15
16question = """Let \(x, y\), and \(z\) be positive real numbers satisfying the system of equations:
17\[
18\begin{array}{c}
19\sqrt{2 x-x y}+\sqrt{2 y-x y}=1 \\
20\sqrt{2 y-y z}+\sqrt{2 z-y z}=\sqrt{2} \\
21\sqrt{2 z-z x}+\sqrt{2 x-z x}=\sqrt{3} .
22\end{array}
23\]
24Then \(\left[(1-x)(1-y)(1-z)\right]^{2}\) can be written as \(\frac{m}{n}\), where \(m\) and \(n\) are relatively prime positive integers. Find \(m+n\)."""
25ds_prompt="<|User|>\n" + question + "<|Assistant|>\n"
26output = model.generate(ds_prompt, sampling_params=sampling_params)
27print(output[0].outputs[0].text)
28
1@article{yang2025reasonflux,
2 title={ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates},
3 author={Yang, Ling and Yu, Zhaochen and Cui, Bin and Wang, Mengdi},
4 journal={arXiv preprint arXiv:2502.06772},
5 year={2025}
6}