{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/openthoughts-data-recipes-for-reasoning","title":"OpenThoughts: Data Recipes for Reasoning Models","arxiv_id":"2506.04178","date":"2025-06-04","proceeding":null,"authors":["Etash Guha","Ryan Marten","Sedrick Keh","Negin Raoof","Georgios Smyrnis","Hritik Bansal","Marianna Nezhurina","Jean Mercat","Trung Vu","Zayne Sprague","Ashima Suvarna","Benjamin Feuer","Liangyu Chen","Zaid Khan","Eric Frankel","Sachin Grover","Caroline Choi","Niklas Muennighoff","Shiye Su","Wanjia Zhao","John Yang","Shreyas Pimpalgaonkar","Kartik Sharma","Charlie Cheng-Jie Ji","Yichuan Deng","Sarah Pratt","Vivek Ramanujan","Jon Saad-Falcon","Jeffrey Li","Achal Dave","Alon Albalak","Kushal Arora","Blake Wulfe","Chinmay Hegde","Greg Durrett","Sewoong Oh","Mohit Bansal","Saadia Gabriel","Aditya Grover","Kai-Wei Chang","Vaishaal Shankar","Aaron Gokaslan","Mike A. Merrill","Tatsunori Hashimoto","Yejin Choi","Jenia Jitsev","Reinhard Heckel","Maheswaran Sathiamoorthy","Alexandros G. Dimakis","Ludwig Schmidt"],"abstract":"Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often rely on proprietary datasets with little to no public information available. To address this, the goal of the OpenThoughts project is to create open-source datasets for training reasoning models. After initial explorations, our OpenThoughts2-1M dataset led to OpenThinker2-32B, the first model trained on public reasoning data to match DeepSeek-R1-Distill-32B on standard reasoning benchmarks such as AIME and LiveCodeBench. We then improve our dataset further by systematically investigating each step of our data generation pipeline with 1,000+ controlled experiments, which led to OpenThoughts3. Scaling the pipeline to 1.2M examples and using QwQ-32B as teacher yields our OpenThoughts3-7B model, which achieves state-of-the-art results: 53% on AIME 2025, 51% on LiveCodeBench 06/24-01/25, and 54% on GPQA Diamond - improvements of 15.3, 17.2, and 20.5 percentage points compared to the DeepSeek-R1-Distill-Qwen-7B. All of our datasets and models are available on https://openthoughts.ai.","url_abs":"https://arxiv.org/abs/2506.04178v2","url_pdf":"https://arxiv.org/pdf/2506.04178v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"openthoughts-data-recipes-for-reasoning","repo_url":"https://github.com/open-thoughts/open-thoughts","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"math","task_name":"Math"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2506.04178","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.04178"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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