Curating the best open reasoning datasets
A collaboration led by Bespoke Labs and the DataComp community
Our first goal is to curate a reasoning dataset to train state-of-the-art small reasoning models that surpass DeepSeek-R1-Distill-Qwen-32B and DeepSeek-R1-Distill-Qwen-7B on math and code reasoning benchmarks.
- [2025/06/04] 🎉🎉🎉 We release our OpenThoughts paper!
- [2025/06/04] 🎉🎉🎉 OpenThinker3 is released!
- [2025/05/09] 🎉 Join our Discord community to discuss OpenThoughts and connect with other users!
- [2025/04/07] 🎉 OpenThoughts2-1M dataset is the #1 trending dataset on Hugging Face.
- [2025/04/03] 🎉 OpenThinker2 has arrived: OpenThoughts2-1M, OpenThinker2-7B, OpenThinker2-32B.
- [2025/03/13] 🎉 We release an analysis of reasoning models on Alice in Wonderland.
- [2025/02/16] 🎉 OpenThinker on Ollama reaches 400k downloads.
- [2025/02/14] 🎉 Chat with OpenThinker in the online playground.
- [2025/02/13] 🎉 OpenThinker is now available on Ollama for easy local inference.
- [2025/02/12] 🎉 We release OpenThinker-32B, the best open-data reasoning model.
- [2025/02/02] 🎉 OpenThoughts-114k dataset is the #1 trending dataset on Hugging Face.
- [2025/01/30] 🎉 Reasoning benchmarks are added to Evalchemy and compared to publicly reported scores.
- [2025/01/28] 🎉 Open Thoughts launches with OpenThoughts-114k dataset and OpenThinker-7B model.
- [2025/01/27] 🎉 Bespoke-Stratos-17k dataset is the #2 trending dataset on Hugging Face.
- [2025/01/22] 🎉 Bespoke-Stratos-17k dataset and Bespoke-Stratos-32B model are announced.
Our OpenThinker3-7B model trained on OpenThoughts3-1.2M is the state-of-the-art open-data 7B reasoning model. The numbers reported in the table below are evaluated with our open-source tool Evalchemy.
| Model | Data | AIME24 | AIME25 | AMC23 | MATH500 | HMMT O2/25 | LCB 06/24-01/25 | CodeElo | CodeForces | GPQA-D | JEEBench |
|---|---|---|---|---|---|---|---|---|---|---|---|
| OpenThinker-7B | ✅ | 30.7 | 22.0 | 72.5 | 82.8 | 15.7 | 26.1 | 11.1 | 14.9 | 38.6 | 45.3 |
| OpenThinker2-7B | ✅ | 60.7 | 38.7 | 89.8 | 87.6 | 24.7 | 40.6 | 22.8 | 26.6 | 47.0 | 65.1 |
| OpenThinker3-7B | ✅ | 69.0 | 53.3 | 93.5 | 90.0 | 42.7 | 51.7 | 31.0 | 32.2 | 53.7 | 72.4 |
| DeepSeek-R1-Distill-Qwen-32B | ❌ | 51.3 | 38.0 | 92.0 | 88.0 | 25.0 | 34.5 | 19.9 | 21.1 | 33.2 | 50.4 |
| OpenR1-Distill-7B | ✅ | 57.7 | 39.7 | 87.0 | 88.0 | 25.7 | 30.7 | 30.1 | 29.3 | 58.9 | 68.7 |
| Llama-3.1-Nemotron-Nano-8B-v1 | ✅ | 62.0 | 48.0 | 94.0 | 89.4 | 26.7 | 50.9 | 30.9 | 32.9 | 52.9 | 70.7 |
| AceReason-Nemotron-7B | ✅ | 71.0 | 50.7 | 93.8 | 89.8 | 33.3 | 44.3 | 32.9 | 30.9 | 52.9 | 64.3 |
To mitigate variance in evaluation accuracy, we compute average scores over multiple evaluation runs with different seeds. More details can be found in our OpenThoughts paper.
We are fully open-source. Our model weights, datasets, data generation code, evaluation code, and training code are all publicly available.
make install
poetry shell
Set the DeepSeek API key:
export DEEPSEEK_API_KEY=your_api_key
Set HF_ORG to your organization id. Set HF_PRIVATE=true if you want to push to a private repo.
export HF_ORG=your_org_id
export HF_PRIVATE=false
The OpenThoughts3-1.2M dataset consists of 850,000 math questions, 250,000 code questions, and 100,000 science questions. As opposed to previous OpenThoughts models that used R1 annotations, OpenThoughts3's reasoning traces are generated with QwQ-32B. This dataset is the result of 1000+ experiments to test out various design choices involved in dataset curation. More details can be found in our OpenThoughts paper.
The OpenThoughts2-1M dataset is a combination of OpenThoughts-114k, OpenR1-Math, and our newly generated math and code reasoning data. We generate the additional math and code data by ablating on 26 different question generation methodologies and sampling from the highest performing ones.
The recipe is outlined below:
More details can be found in our blog post.
For OpenThoughts-114k, we generate data for the following domains:
- Code
- Math
- Science
- Puzzle
The recipe is outlined below:
More instructions are in open_thoughts/README.md.
Training and evaluation code coming soon.
- 📝 OpenThoughts Paper
- 📊 OpenThoughts3-1.2M and OpenThinker3-7B Blog Post
- 💻 Open Thoughts GitHub Repository
- 🧠 OpenThoughts3-1.2M dataset
- 🤖 OpenThinker3-7B model
We are a team of researchers and engineers from Bespoke Labs, Stanford, University of California Berkeley, University of Washington, UT Austin, Juelich Supercomputing Center (JSC), LAION, UCLA, UNC Chapel Hill, UT Austin, and Toyota Research Institute united around building the best datasets (and thus the best models). See our previous works at datacomp.ai and mlfoundations.
Open Thoughts is supported by
- Bespoke Labs
- Toyota Research Institute
- Lambda Labs
- NSF IFML
- UT Austin Machine Learning Lab
- Juelich Supercomputing Center
Make an edit to add your project!
Join our Discord community to discuss OpenThoughts and connect with other users!
What the open source community is building with OpenThoughts:
- Light-R1-SFT includes examples from OpenThoughts-114k and is used to train Light-R1-14B-DS, Light-R1-32B, Light-R1-7B-DS, Light-R1-32B-DS
- Traceback-12B is a reasoning model trained on a dataset that includes OpenThoughts-114k and Bespoke-Stratos-17k
- 190+ public models on Hugging Face have been trained using OpenThoughts-114k
- 100+ public models on Hugging Face have been trained using Bespoke-Stratos-17k
- Sky-T1 uses Bespoke-Stratos-17k for their R1 SFT experiments
- Ollama has created quantized versions of the OpenThinker-7B and OpenThinker-32B models, for running locally on your laptop
- CuratedThoughts is a filtered version of OpenThoughts-114k to make it suitable for RL training
- OpenThoughts-114k-math is a filtered version of the math subset in OpenThoughts-114k using Math-Verify verification on top of our LLM Judge with GT verification
- SmallThoughts regenerates a 50k version of OpenThoughts-114k using a fork of this repo
- AM-DeepSeek-R1-Distilled-1.4M is a state of the art reasoning dataset mix containing OpenThoughts-114k and Bespoke-Stratos-17k
- Marin 8B of the Stanford Marin Project, a collaborative effort to develop open-source foundation models, is trained on Bespoke-Stratos-17k.
@misc{guha2025openthoughtsdatarecipesreasoning,
title={OpenThoughts: Data Recipes for Reasoning Models},
author={Etash Guha and Ryan Marten and Sedrick Keh and Negin Raoof and Georgios Smyrnis and Hritik Bansal and Marianna Nezhurina and Jean Mercat and Trung Vu and Zayne Sprague and Ashima Suvarna and Benjamin Feuer and Liangyu Chen and Zaid Khan and Eric Frankel and Sachin Grover and Caroline Choi and Niklas Muennighoff and Shiye Su and Wanjia Zhao and John Yang and Shreyas Pimpalgaonkar and Kartik Sharma and Charlie Cheng-Jie Ji and Yichuan Deng and Sarah Pratt and Vivek Ramanujan and Jon Saad-Falcon and Jeffrey Li and Achal Dave and Alon Albalak and Kushal Arora and Blake Wulfe and Chinmay Hegde and Greg Durrett and Sewoong Oh and Mohit Bansal and Saadia Gabriel and Aditya Grover and Kai-Wei Chang and Vaishaal Shankar and Aaron Gokaslan and Mike A. Merrill and Tatsunori Hashimoto and Yejin Choi and Jenia Jitsev and Reinhard Heckel and Maheswaran Sathiamoorthy and Alexandros G. Dimakis and Ludwig Schmidt},
year={2025},
eprint={2506.04178},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2506.04178},
}