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--- |
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license: mit |
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language: |
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- en |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
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pipeline_tag: text-generation |
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library_name: transformers |
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--- |
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<div align="center"> |
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<span style="font-family: default; font-size: 1.5em;">FastCuRL-1.5B-Preview</span> |
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</div> |
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## FastCuRL Overview |
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### 2025-03-17 |
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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. |
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Code: https://github.com/nick7nlp/FastCuRL |
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### 2025-03-21 |
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Paper: https://arxiv.org/abs/2503.17287 |
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## Key Results |
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We report Pass@1 accuracy averaged over 16 samples for each problem. |
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| Model | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | OlympiadBench | Avg. | |
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|-------|-----------|-----------|-----------|--------------|---------------|------| |
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| Qwen2.5-Math-7B-Instruct | 13.3 | 79.8 | 50.6 | 34.6 | 40.7 | 43.8 | |
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| rStar-Math-7B | 26.7 | 78.4 | 47.5 | - | 47.1 | - | |
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| Eurus-2-7B-PRIME | 26.7 | 79.2 | 57.8 | 38.6 | 42.1 | 48.9 | |
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| Qwen2.5-7B-SimpleRL | 26.7 | 82.4 | 62.5 | <strong>39.7</strong> | 43.3 | 50.9 | |
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| DeepSeek-R1-Distill-Qwen-1.5B | 28.8 | 82.8 | 62.9 | 26.5 | 43.3 | 48.9 | |
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| Still-1.5B | 32.5 | 84.4 | 66.7 | 29.0 | 45.4 | 51.6 | |
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| DeepScaleR-1.5B-Preview | 43.1 | 87.8 | 73.6 | 30.2 | 50.0 | 57.0 | |
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| <strong>FastCuRL-1.5B-Preview</strong> | <strong>43.1</strong> | <strong>88.0</strong> | <strong>74.2</strong> | 31.6 | <strong>50.4</strong> | <strong>57.5</strong> | |
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## Training Data |
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Following DeepScaleR, our training dataset consists of 40,315 unique problem-answer pairs compiled from: |
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- AIME problems (1984-2023) |
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- AMC problems (before 2023) |
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- Omni-MATH dataset |
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- Still dataset |
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## Acknowledgements |
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- 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). |
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- Our model is trained on top of [`DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B). |
