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. (31 additional authors not shown)

Keywords: Reasoning, Data, LLM

TL;DR: Data pipeline analysis for training reasoning models

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 train-
ing 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. 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 genera-
tion 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-of-the-art results: 53% on
AIME 2025, 51% on LiveCodeBench 06/24-01/25, and 54% on GPQA Dia-
mond – 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
openthoughts.ai.

Primary Area: foundation or frontier models, including LLMs

Submission Number: 5295