---
license: mit
language:
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
pipeline_tag: text-generation
library_name: transformers
---

<div align="center">
<span style="font-family: default; font-size: 1.5em;">FastCuRL-1.5B-Preview</span>
</div>

## FastCuRL Overview

### 2025-05-23

We release **FastCuRL-1.5B-V3** and **FastCuRL-1.5B-V2**.

### 2025-03-17

We release **FastCuRL-1.5B-Preview**, a slow-thinking reasoning model that **outperforms** the previous SoTA *DeepScaleR-1.5B-Preview* with **50% training steps**! We adapt a novel curriculum-guided iterative lengthening reinforcement learning to the *DeepSeek-R1-Distill-Qwen-1.5B* and observe continuous performance improvement as training steps increase. To better reproduce our work and advance research progress, we open-source our code, model, and data.

Code: https://github.com/nick7nlp/FastCuRL

### 2025-03-21

Paper: https://arxiv.org/abs/2503.17287

## Key Results

We report Pass@1 accuracy averaged over 16 samples for each problem.

| Model | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | OlympiadBench | Avg. |
|-------|-----------|-----------|-----------|--------------|---------------|------|
| Qwen2.5-Math-7B-Instruct | 13.3 | 79.8 | 50.6 | 34.6 | 40.7 | 43.8 |
| rStar-Math-7B | 26.7 | 78.4 | 47.5 | - | 47.1 | - |
| Eurus-2-7B-PRIME | 26.7 | 79.2 | 57.8 | 38.6 | 42.1 | 48.9 |
| Qwen2.5-7B-SimpleRL | 26.7 | 82.4 | 62.5 | <strong>39.7</strong> | 43.3 | 50.9 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.8 | 82.8 | 62.9 | 26.5 | 43.3 | 48.9 |
| Still-1.5B | 32.5 | 84.4 | 66.7 | 29.0 | 45.4 | 51.6 |
| DeepScaleR-1.5B-Preview | 43.1 | 87.8 | 73.6 | 30.2 | 50.0 | 57.0 |
| <strong>FastCuRL-1.5B-Preview</strong> | 43.1 | 88.0 | 74.2 | 31.6 | 50.4 | 57.5 |
| <strong>FastCuRL-1.5B-V2</strong> | 47.5 | 89.3 | 77.0 | 32.8 | 53.3 | 60.0 |
| <strong>FastCuRL-1.5B-V3</strong> | <strong>49.6</strong> | <strong>90.5</strong> | <strong>78.5</strong> | <strong>34.7</strong> | <strong>54.5</strong> | <strong>61.6</strong> |

## Training Data
Following DeepScaleR, our training dataset consists of 40,315 unique problem-answer pairs compiled from:
- AIME problems (1984-2023)
- AMC problems (before 2023)
- Omni-MATH dataset
- Still dataset

## Acknowledgements

- Our training experiments are powered by our heavily modified fork of [verl](https://github.com/volcengine/verl) and [deepscaler](https://github.com/agentica-project/deepscaler).
- Our model is trained on top of [`DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).