OpenThoughts-Agent：代理式模型的数据配方
OpenThoughts-Agent: Data Recipes for Agentic Models
摘要
代理式语言模型极大地拓展了人工智能的应用范围，但关于如何整理适合各种任务处理的训练数据，目前却鲜有公开信息。像SWE-Smith、SERA和Nemotron-Terminal这样的开源项目通常只针对单一评估标准，因此很难找到方法来训练能够适用于多种任务的模型。OpenThoughts-Agent项目则通过完全开源的数据处理流程来解决这一问题。我们进行了超过100次受控的消融实验，以系统地研究该处理流程的各个阶段，从而了解任务来源和多样性的重要性。随后，我们从该处理流程中收集了10万个样本作为训练集，并在该数据集上对Qwen3-32B模型进行微调，结果显示，该模型在七种代理式任务评估中平均准确率达到了44.8%，比现有最优秀的开源代理式模型Nemotron-Terminal-32B的准确率提升了3.9个百分点（后者为40.9%）。此外，我们的训练数据具有出色的可扩展性，在各种训练集规模下，其性能都优于其他开源数据集。我们在openthought.ai上公开了这些训练集、数据处理流程、实验数据和模型，以支持未来关于代理式模型训练的开放研究。
English 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.