OpenThoughts: Data Recipes for Reasoning Models

Etash Kumar Guha, Ryan Marten, Sedrick Keh, Negin Raoof, Georgios Smyrnis, Hritik Bansal, Marianna Nezhurina, Jean Mercat, Trung Vu, Zayne Rea Sprague, Ashima Suvarna, Benjamin Feuer, Leon Liangyu Chen, Zaid Khan, Eric Frankel, Sachin Grover, Caroline Choi, Niklas Muennighoff, Shiye Su, Wanjia Zhao et al. (30 additional authors not shown)

Keywords: Reasoning, LLM, Data

TL;DR: Analysis for finding the best recipe for generating postraining reasoning data for training LLMs.

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 OpenThinker3-7B model, which achieves state-ofthe-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 REDACTED.

Submission Number: 134