DeepScaleR
🚀 Democratizing Reinforcement Learning for LLMs 🌟
DeepScaleR-1.5B-Preview is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 43.1% Pass@1 accuracy on AIME 2024, representing a 15% improvement over the base model (28.8%) and surpassing OpenAI’s O1-Preview performance with just 1.5B parameters.
| Model | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | Olympiad Bench | Avg. |
|---|---|---|---|---|---|---|
| DeepScaleR-1.5B-Preview | 43.1 | 87.8 | 73.6 | 30.2 | 50.0 | 57.0 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.8 | 82.8 | 62.9 | 26.5 | 43.3 | 48.9 |
| O1-Preview | 40.0 | 81.4 | - | - | - | - |
Data
Our training dataset consists of approximately 40,000 unique problem-answer pairs compiled from:
- AIME problems (1984-2023)
- AMC problems (prior to 2023)
- Omni-MATH dataset
- Still dataset
Evaluation
We evaluate our model on competition-level mathematics benchmarks, including AIME 2024, AMC 2023, MATH-500, Minerva Math, and OlympiadBench. Below, Pass@1 accuracy is reported, averaged over 16 samples for each problem.
| Model | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | OlympiadBench | Avg. |
|---|---|---|---|---|---|---|
| Qwen-2.5-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 | 39.7 | 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 |
| O1-Preview | 40.0 | 81.4 | - | - | - | - |
We compare DeepScaleR with the base DeepSeek model we use, as well as recent academic works exploring RL for reasoning tasks. DeepScaleR significantly outperforms the base model across all benchmarks, achieving a 14.4% absolute gain on AIME2024 and an 8.1% overall improvement. Additionally, DeepScaleR surpasses recent academic works such as rSTAR, Prime, and SimpleRL, which are finetuned from 7B models. DeepScaleR achieves O1-preview-level performance with only 1.5B parameters—a remarkable efficiency gain.