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qwen2 architecture, making it compatible with popular libraries and inference engines such as vllm or sglang."Think for 10000 tokens before giving a response." to your instruction. As reported in our technical report, the model did not train for long enough to fully learn the length control objective, which is why results won't differ strongly if you specify lengths other than 10,000. If you wish to do so, you can expect the best results with 2000, 4000, 6000 and 8000, as these were the other target lengths present during training.
| Model | AIME24 | AIME25 | LiveCodeBench (v5) | GPQA-Diamond | IFEval |
|---|---|---|---|---|---|
| INTELLECT-2 | 78.8 | 64.9 | 67.8 | 66.8 | 81.5 |
| QwQ-32B | 76.6 | 64.8 | 66.1 | 66.3 | 83.4 |
| Qwen-R1-Distill-32B | 69.9 | 58.4 | 55.1 | 65.2 | 72.0 |
| Deepseek-R1 | 78.6 | 65.1 | 64.1 | 71.6 | 82.7 |
@misc{primeintellectteam2025intellect2reasoningmodeltrained,
title={INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning},
author={Prime Intellect Team and Sami Jaghouar and Justus Mattern and Jack Min Ong and Jannik Straube and Manveer Basra and Aaron Pazdera and Kushal Thaman and Matthew Di Ferrante and Felix Gabriel and Fares Obeid and Kemal Erdem and Michael Keiblinger and Johannes Hagemann},
year={2025},
eprint={2505.07291},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2505.07291},
}