# OpenThoughts-Agent: Data Recipes for Agentic Models

arXiv: https://arxiv.org/abs/2606.24855 · HTML: https://mlsanity.com/paper/2606.24855/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning for agents, planning and code, one of 17 directions in LLM reasoning.
- Builds on 0 of its 50 references that are in the map.
- First posted on arXiv 23 Jun 2026.

## Placed in

- LLM reasoning / [Reasoning for agents, planning and code](https://mlsanity.com/lr/reasoning-for-agents-planning-and-code/)
- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Built on by (most cited first, 11 of 11)

- [NexForge: Scaling Agent Capabilities through Requirement-Driven Task Synthesis for LLMs](https://mlsanity.com/paper/2607.14186/) (2026)
- [MindForge: Teaching Small Language Models Whole-Life-Cycle Software Engineering via Source-Free Program Synthesis](https://mlsanity.com/paper/2607.27146/) (2026)
- [Learning Generalizable Behaviors for Terminal Agents](https://mlsanity.com/paper/2608.22631/) (2026)
- [Mid-Harness: Scaling Actions Between Model and Harness for Terminal Agents](https://mlsanity.com/paper/2609.39982/) (2026)
- [Self-Supervised Scaling of Terminal Environments for Scientific Domains](https://mlsanity.com/paper/2610.02710/) (2026)
- [Mint-Agent: Introducing Finance-Native Agentic Foundation Models](https://mlsanity.com/paper/2608.16386/) (2026)
- [Rethinking Heterogeneous System Disaggregation for Subquadratic Attention](https://mlsanity.com/paper/2609.13134/) (2026)
- [SkillGym: Internalizing Human Skills into LLMs for Real-World Problem Solving](https://mlsanity.com/paper/2609.27717/) (2026)
- [DataSense-Bench: The First Step Toward an AI Scientist](https://mlsanity.com/paper/2610.12190/) (2026)
- [Scaling Agentic Data for Long-Horizon Terminal Intelligence](https://mlsanity.com/paper/2610.13166/) (2026)
- [ReTerm: Recovery Training for Self-Improving Terminal Agents](https://mlsanity.com/paper/2610.18484/) (2026)

## Abstract (arXiv)

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.