Project | SFT dataset | RL dataset | SFT model | RL model

# OpenThinker-Agent-v1-SFT **OpenThoughts-Agent** is an open-source effort to curate the best datasets for training agents. Our first release includes [datasets](https://huggingface.co/collections/open-thoughts/openthinker-agent), [models](https://huggingface.co/collections/open-thoughts/openthinker-agent) and our [research codebase](https://github.com/open-thoughts/OpenThoughts-Agent). [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) is a model trained for agentic tasks such as **Terminal-Bench 2.0** and **SWE-Bench**. The [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) model is post-trained from [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). It is SFT-ed on the [OpenThoughts-Agent-v1-SFT](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT) dataset, then RL-ed on the [OpenThoughts-Agent-v1-RL](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) dataset. This [OpenThinker-Agent-v1-SFT](https://huggingface.co/open-thoughts/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](https://huggingface.co/open-thoughts/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](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) 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](https://huggingface.co/Qwen/Qwen3-8B) | Terminus-2 | 0.0 | 0.7 | 5.7 | | **[OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1)** | Terminus-2 | 4.9 | 15.7 | 17.3 | | [Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) | Terminus-2 | 1.9 | 5.7 | 10.2 | | [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) | OpenHands | 10.1 | 49.2 | 24.5 | # Data We built [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/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](https://huggingface.co/datasets/open-thoughts/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](https://huggingface.co/datasets/open-thoughts/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: 1. Bad verifiers filter: drop tasks with flaky or excessively slow verifiers. 2. 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](https://open-thoughts.ai/blog/agent) - 💻 [OpenThoughts-Agent GitHub repository](https://github.com/open-thoughts/OpenThoughts-Agent) - 🧠 [OpenThoughts-Agent-v1-SFT dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT) - 🧠 [OpenThoughts-Agent-v1-RL dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL) - 🧠 [OpenThoughts-TB-dev dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TB-dev) - 🤖 [OpenThinker-Agent-v1 model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) - 🤖 [OpenThinker-Agent-v1-SFT model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT) --> 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} } ```