OpenThoughts-Agent: Data Recipes for Agentic Models

The OpenThoughts-Agent (OT-Agent) project has introduced a comprehensive data curation pipeline aimed at enhancing the training of agentic language models. By conducting over 100 controlled ablation experiments, the team assembled a dataset of 100,000 examples, fine-tuning the Qwen3-32B model, which achieved an average accuracy of 44.8% across seven benchmarks, surpassing the previous best open model, Nemotron-Terminal-32B, by 3.9 percentage points. This release, including the training sets and experimental data, provides valuable resources for practitioners aiming to develop more capable and generalizable agentic models.