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library_name: transformers
license: other
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: ReasonFlux-F1-32B
results: []
---
# ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates
Revolutionary template-augmented reasoning paradigm enpowers a 32B model to outperform o1-mini and DeepSeek-R1 distilled models in reasoning tasks.
| Task/Pass@1 | **ReasonFlux-F1-32B** | **ReasonFlux-Zero-32B** | **R1-Distill-32B** | **o1-mini** | **LIMO -32B** | **s1-32B** |
| :------------- | :----------------: | :-------------: | :-------------------: | :-----------------: | :--------: | :--------: |
| MATH500 | **96.0** | 91.2 | 94.3 | 90.0 | 90.6 | 93.0 |
| AIME 2024 | **76.7** | 56.7 | 72.6 | 56.7 | 50.0 | 56.7 |
| AIME 2025 | **53.3** | 37.2 | 46.67 | 50.8 | 37.2 | 49.3 |
| GPQA-Diamond | **67.2** | 61.2 | 62.1 | 60.0 | 65.2 | 59.6 |
# ReasonFlux-F1-32B
> ReasonFlux-F1-32B is our finetuned SOTA-level reasoning LLM by leveraging the template-augmented reasoning trajectories from our [ReasonFlux-Zero](https://arxiv.org/abs/2502.06772).
* Github Repository: [Gen-Verse/ReasonFlux](https://github.com/Gen-Verse/ReasonFlux)
* Paper:[ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates](https://arxiv.org/abs/2502.06772)
* Dataset: [Gen-Verse/ReasonFlux-F1-SFT](https://huggingface.co/datasets/Gen-Verse/ReasonFlux-F1-SFT)
## Evaluation
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](https://github.com/Gen-Verse/ReasonFlux/tree/main/reasonflux-f1).
| Model | AIME2024@pass1 | AIME2025@pass1 | MATH500@pass1 | GPQA@pass1 |
| --------------------------------------- | :--------------: | :--------------: | :-------------: | :----------: |
| QwQ-32B-Preview | 46.7 | 37.2 | 90.6 | 65.2 |
| LIMO-32B | 56.3 | 44.5 | 94.8 | 58.1 |
| s1-32B | 56.7 | 49.3 | 93.0 | 59.6 |
| OpenThinker-32B | 66.0 | 53.3 | 94.8 | 60.1 |
| R1-Distill-32B | 70.0 | 46.7 | 92.0 | 59.6 |
| ReasonFlux-Zero-32B | 56.7 | 37.2 | 91.2 | 61.2 |
| **ReasonFlux-F1-32B** | **76.7** | **53.3** | **96.0** | **67.2** |
## Quick start with VLLM
```python
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = 'Gen-Verse/ReasonFlux-F1'
model = LLM(
model_id,
tensor_parallel_size=8,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
sampling_params = SamplingParams(
max_tokens=32768,
)
# 2022 AIME I Problems/Problem 15
question = """Let \(x, y\), and \(z\) be positive real numbers satisfying the system of equations:
\[
\begin{array}{c}
\sqrt{2 x-x y}+\sqrt{2 y-x y}=1 \\
\sqrt{2 y-y z}+\sqrt{2 z-y z}=\sqrt{2} \\
\sqrt{2 z-z x}+\sqrt{2 x-z x}=\sqrt{3} .
\end{array}
\]
Then \(\left[(1-x)(1-y)(1-z)\right]^{2}\) can be written as \(\frac{m}{n}\), where \(m\) and \(n\) are relatively prime positive integers. Find \(m+n\)."""
ds_prompt="<｜User｜>\n" + question + "<｜Assistant｜>\n"
output = model.generate(ds_prompt, sampling_params=sampling_params)
print(output[0].outputs[0].text)
```
## Citation
```bash
@article{yang2025reasonflux,
title={ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates},
author={Yang, Ling and Yu, Zhaochen and Cui, Bin and Wang, Mengdi},
journal={arXiv preprint arXiv:2502.06772},
year={2025}
}
``` |