AI智能体训练数据配方首次公开
训练一个能完成多种任务的AI智能体，数据配方一直是黑箱。OpenThoughts-Agent项目首次系统公开了数据筛选的全流程：他们做了100多次消融实验，发现任务来源和多样性比数据量更关键。最终用10万条精选数据微调Qwen3-32B模型，在7个智能体基准上平均得分44.8%，比之前最好的开源模型高3.9个百分点。而且数据量越大，优势越明显。这不是你明天能直接用的工具，但如果你关心AI智能体为什么有的聪明有的笨，这篇给出了第一个可复现的答案。
📄 原文摘要(英文)
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.