Create README.md

README.md

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+---
+license: llama3.2
+datasets:
+- OctoThinker/MegaMath-Web-Pro-Max
+- LLM360/MegaMath
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B
+pipeline_tag: text-generation
+---
+
+# [OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling](https://arxiv.org/abs/2506.20512)
+
+
+
+## OctoThinker-3B-Long-Base
+
+
+The OctoThinker family is built on carefully studied mid-training insights, starting from the Llama-3 family, to create a reinforcement learning–friendly base language model.
+
+### Training Recipe
+
+<div style="display: flex; justify-content: left; gap: 20px;">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/62cbeb2d72dfd24b86bdf977/fGkg_-5a2y8tI20025SOu.png" alt="Data Pipeline" style="width:90%;">
+</div>
+
+
+
+
+### Evaluation Results
+
+Note that we adopt the few-shot prompting evaluation for these base language models.
+
+
+<div style="display: flex; justify-content: left; gap: 20px;">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/62cbeb2d72dfd24b86bdf977/UCZ9MahRYqLY0iKjiWMqS.png" alt="Data Pipeline" style="width:80%;">
+
+</div>
+
+
+### More about OctoThinker
+
+
+<div style="display: flex; justify-content: left; gap: 20px;">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/62cbeb2d72dfd24b86bdf977/bn85CEB_DW6azJ7KJp11Q.png" alt="Data Pipeline" style="width:100%;">
+</div>
+
+
+## Citation
+
+Check out our [paper](https://arxiv.org/abs/2506.20512) for more details. If you use our models, datasets or find our work useful, please cite
+
+```
+@article{wang2025octothinker,
+  title={OctoThinker: Mid-training Incentivizes Reinforcement Learning Scaling},
+  author={Wang, Zengzhi and Zhou, Fan and Li, Xuefeng and Liu, Pengfei},
+  year={2025},
+  journal={arXiv preprint arXiv:2506.20512},
+  note={Preprint}
+}
+```