OpenThinker-Agent-v1-SFT
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 OpenThinker-Agent-v1-SFT model is the model after the SFT stage. For the model after both SFT and RL stages, see OpenThinker-Agent-v1.
- Homepage: https://www.openthoughts.ai/blog/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.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 16
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.98) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7.0
Framework versions
- Transformers 4.56.0
- Pytorch 2.9.0+cu128
- Datasets 4.4.1
- Tokenizers 0.22.1
Links
- 🌐 OpenThoughts-Agent project page
- 💻 OpenThoughts-Agent GitHub repository
- 🧠 OpenThoughts-Agent-v1-SFT dataset
- 🧠 OpenThoughts-Agent-v1-RL dataset
- 🧠 OpenThoughts-TB-dev dataset
- 🤖 OpenThinker-Agent-v1 model
- 🤖 OpenThinker-Agent-v1-SFT model --> this model
Citation
@misc{openthoughts-agent,
author = {Team, OpenThoughts-Agent},
month = Dec,
title = {{OpenThoughts-Agent}},
howpublished = {https://www.open-thoughts.ai/blog/agent},
year = {2025}
}