base_model, license, pipeline_tag, library_name, tags

base_model	license	pipeline_tag	library_name	tags
nvidia/Nemotron-Research-Reasoning-Qwen-1.5B	cc-by-nc-4.0	text-generation	transformers	https://arxiv.org/abs/2511.07317

Note

For full information, go check out the RLVE paper here.

Nemotron Research Reasoning Qwen 1.5B v2 RLVE

This model is trained on top of Nemotron-Research-Reasoning-Qwen-1.5B using RLVE. For more details on RLVE, check out the figure below, or see our paper for more details.

Evaluation Results

We provide evaluation instructions in our repository.

Benchmark	AIME 2024 (Avg@64)	AIME 2025 (Avg@64)	OMEGA-500 (Avg@4)	OlympiadBench (Avg@4)	BBEH (Avg@4)	LiveCodeBench-v6 (Pass@8)
Nemotron-Research-Reasoning-Qwen-1.5B-v2 (starting model)	51.93	33.96	24.15	57.85	7.27	25.90
Nemotron-Research-Reasoning-Qwen-1.5B-v2-RLVE	56.51	39.84	27.75	60.56	9.10	27.49

Intended uses & limitations

CC-BY-NC-4.0, inherited from starting model (Nemotron-Research-Reasoning-Qwen-1.5B-v2).

Training

For training details and hyperparameters. please see our repository.

In particular, you can rerun training for this model with this command (after setting up the repository):

bash scripts/training/Nemotron-Research-Reasoning-Qwen-1.5B-v2/rlve/num-environment=400.sh RLVE

Links

• 📝 RLVE Paper
• 💻 RLVE GitHub Repository
• 🤖 Nemotron-Research-Reasoning-Qwen-1.5B-v2-RLVE - this model.
• 🤖 OpenThinker3-1.5B-RLVE

Citation

@article{zeng2025rlve,
  title={RLVE: Scaling Up Reinforcement Learning for Language Models with Adaptive Verifiable Environments},
  author={Zeng, Zhiyuan and Ivison, Hamish and Wang, Yiping and Yuan, Lifan and Li, Shuyue Stella and Ye, Zhuorui and Li, Siting and He, Jacqueline and Zhou, Runlong and Chen, Tong and Zhao, Chenyang and Tsvetkov, Yulia and Du, Simon Shaolei and Jaques, Natasha and Peng, Hao and Koh, Pang Wei and Hajishirzi, Hannaneh},
  journal={arXiv preprint 2511.07317},
  year={2025}
}