OpenThoughts-Agent:开源数据流水线训练智能体模型
阅读原文· arxiv.orgOpenThoughts-Agent(OT-Agent)项目提出一套完全开源的数据 curation 流水线,专门用于训练智能体模型。研究团队通过 100 余项对照消融实验,系统探索了任务来源与多样性的影响,并构建了包含 10 万条样本的数据集。基于该数据集微调 Qwen3-32B 后,模型在 7 项智能体基准测试中平均准确率为 44.8%,比现有最强的开源数据智能体模型 Nemotron-Terminal-32B(40.9%)高 3.9 个百分点。训练数据展现出强扩展性,同等计算资源下各数据规模均优于其他开源数据集。所有数据、流水线、实验记录及模型已在 openthoughts.ai 公开发布。
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.