OpenThinker-Agent-v1

OpenThoughts-Agent is an open-source effort to curate the best datasets for training agents. Our first release includes datasets, models and our research codebase.

OpenThinker-Agent-v1 is a model trained for agentic tasks such as Terminal-Bench 2.0 and SWE-Bench.

The OpenThinker-Agent-v1 model is post-trained from Qwen/Qwen3-8B. It is SFT-ed on the OpenThoughts-Agent-v1-SFT dataset, then RL-ed on the OpenThoughts-Agent-v1-RL dataset.

This model is the final model after both SFT and RL. For the model after the SFT stage only, see OpenThinker-Agent-v1-SFT.

Homepage: https://www.open-thoughts.ai/agent
Repository: https://github.com/open-thoughts/OpenThoughts-Agent

OpenThinker-Agent-v1 Model Performance

Our OpenThinker-Agent-v1 model is the state-of-the-art model at its scale on agent benchmarks.

Model	Harness	Terminal-Bench 2.0	SWE-Bench Verified	OpenThoughts-TB-Dev
Qwen3-8B	Terminus-2	0.0	0.7	5.7
OpenThinker-Agent-v1	Terminus-2	4.9	15.7	17.3
Qwen3-32B	Terminus-2	1.9	5.7	10.2
Qwen/Qwen3-Coder-30B-A3B-Instruct	OpenHands	10.1	49.2	24.5

Data

We built OpenThinker-Agent-v1 in two stages: supervised fine-tuning, followed by reinforcement learning. Each stage required its own data pipeline – RL tasks (instructions, environments, and verifiers) and SFT traces from strong teacher agents completing tasks.

OpenThoughts-Agent-v1-SFT is an SFT trace dataset containing approximately 15,200 traces drawn from two different data sources we curate:

nl2bash: Simple synthetically generated tasks where the agent has to format shell commands effectively
InferredBugs: A set of bugs in C# and Java collected by Microsoft that we turned into tasks

OpenThoughts-Agent-v1-RL is an RL dataset containing ~720 tasks drawn from the nl2bash verified dataset.

To stabilize training, we built a three-stage filtration pipeline that prunes tasks before they ever hit the learner:

Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
Environment stability: remove tasks whose containers take too long to build or tear down. Optional difficulty filter: discard tasks that even a strong model (GPT-5 Codex) cannot solve in a single pass.

Citation

@misc{openthoughts-agent,
  author = {Team, OpenThoughts-Agent},
  month = Dec,
  title = {{OpenThoughts-Agent}},
  howpublished = {https://open-thoughts.ai/agent},
  year = {2025}
}