OpenThoughts-Agent: Data Recipes for Agentic Models

2026-06-23

AI Summary

This work addresses the lack of open, generalizable methodologies for constructing training data for intelligent agents—a key limitation hindering their generalization across diverse tasks. The study presents the first systematic investigation into agent training data construction, introducing an open-source and scalable data recipe. Through multi-source task sampling, diversity optimization, and controlled ablation studies, the authors rigorously analyze how task provenance and data composition influence model performance. A 100K-sample training set built using this approach achieves an average accuracy of 44.8% across seven agent benchmarks, outperforming the strongest existing open-source model by 3.9 percentage points. The method consistently maintains superior performance across varying data scales, demonstrating strong generalization and practical utility.

Abstract

Agentic language models dramatically expand the applications of AI yet little is publicly known about how to curate training data for broadly capable agents. Existing open efforts such as SWE-Smith, SERA, and Nemotron-Terminal typically target a single benchmark, leaving open the question of how to train models that generalize across diverse agentic tasks. The OpenThoughts-Agent (OT-Agent) project addresses this gap with a fully open data curation pipeline for training agentic models. We conduct more than 100 controlled ablation experiments to systematically investigate each stage of the pipeline, yielding insights on the importance of task sources and diversity. We then assemble a training set of 100K examples from our pipeline and fine-tune Qwen3-32B on this dataset, which yields an average accuracy of 44.8% across seven agentic benchmarks and a 3.9 percentage point improvement over the strongest existing open data agentic model (Nemotron-Terminal-32B, 40.9%). Moreover, our training data exhibits strong scaling properties, outperforming alternative open datasets at every training set size in compute-controlled comparisons. We publicly release our training sets, data pipeline, experimental data, and models at openthoughts.ai to support future open research on agentic model training.

Problem

• agentic models
• training data curation
• task generalization
• data diversity
• open research

Innovation

• agentic models
• data curation
• open-source training data
• scaling properties
• controlled ablation experiments