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results: []
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## Intended uses & limitations
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## Training procedure
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### Training hyperparameters
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 7.0
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### Training results
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### Framework versions
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- Transformers 4.56.0
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- Pytorch 2.9.0+cu128
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- Datasets 4.4.1
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- Tokenizers 0.22.1
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results: []
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---
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---
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base_model:
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- Qwen/Qwen3-8B
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datasets:
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- OpenThoughts-Agent-v1-SFT
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- OpenThoughts-Agent-v1-RL
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library_name: transformers
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license: apache-2.0
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model-index:
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- name: OpenThinker-Agent-v1
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results: []
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pipeline_tag: text-generation
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tags:
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- agents
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- terminal
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- code
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- software-engineering
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---
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<p align="center">
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<img src="https://huggingface.co/datasets/open-thoughts/OpenThoughts1-Agent-SFT/resolve/main/ota-logo.png" width="50%">
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</p>
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<p align="center">
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<a href="https://open-thoughts.ai/agent" style="margin-right: 24px;">project</a> |
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<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT" style="margin-right: 24px; margin-left: 24px;">SFT dataset</a> |
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<a href="https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL" style="margin-right: 24px; margin-left: 24px;">RL dataset</a> |
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<a href="https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT" style="margin-right: 24px; margin-left: 24px;">SFT model</a> |
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<a href="https://huggingface.co/open-thoughts/OpenThinker-Agent-v1" style="margin-left: 24px;">RL model</a>
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</p>
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# OpenThinker-Agent-v1
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**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.
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[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**.
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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).
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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.
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This 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).
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- **Homepage:** https://www.open-thoughts.ai/agent
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- **Repository:** https://github.com/open-thoughts/OpenThoughts-Agent
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# OpenThinker-Agent-v1 Model Performance
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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.
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| Model | Terminal-Bench 2.0 | SWE-Bench | OpenThoughts-TB-Dev |
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| ----------------------------------------------------------------------------------------------- | ------------------ | --------- | ------------------- |
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| **[OpenThinker-Agent-v1](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL)** | 4.9 | 15.7 | 17.3 |
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| [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) | 10.1 | 51.6 | 24.5 |
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# Data
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We built [OpenThinker-Agent-v1](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1) in two stages: **supervised fine-tuning**, followed by **reinforcement learning**.
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Each stage required its own data pipeline – RL tasks (instructions, environments, and verifiers) and SFT traces from strong teacher agents completing tasks.
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[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:
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- **nl2bash**: Simple synthetically generated tasks where the agent has to format shell commands effectively
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- **InferredBugs**: A set of bugs in C# and Java collected by Microsoft that we turned into tasks
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[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.
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To stabilize training, we built a three-stage filtration pipeline that prunes tasks before they ever hit the learner:
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1. Bad verifiers filter: drop tasks with flaky or excessively slow verifiers.
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2. Environment stability: remove tasks whose containers take too long to build or tear down.
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Optional difficulty filter: discard tasks that even a strong model (GPT-5 Codex) cannot solve in a single pass.
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### Training hyperparameters
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 7.0
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### Framework versions
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- Transformers 4.56.0
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- Pytorch 2.9.0+cu128
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- Datasets 4.4.1
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- Tokenizers 0.22.1
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# Links
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- 🌐 [OpenThoughts-Agent Project Page](https://open-thoughts.ai/agent)
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- 💻 [OpenThoughts-Agent GitHub Repository](https://github.com/open-thoughts/OpenThoughts-Agent)
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- 🧠 [OpenThoughts-Agent-v1-SFT dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-SFT)
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- 🧠 [OpenThoughts-Agent-v1-RL dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-Agent-v1-RL)
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- 🧠 [OpenThoughts-TB-dev dataset](https://huggingface.co/datasets/open-thoughts/OpenThoughts-TB-dev)
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- 🤖 [OpenThinker-Agent-v1 model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1)
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- 🤖 [OpenThinker-Agent-v1-SFT model](https://huggingface.co/open-thoughts/OpenThinker-Agent-v1-SFT) --> this model
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# Citation
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```
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@misc{openthoughts-agent,
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author = {Team, OpenThoughts-Agent},
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month = Dec,
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title = {{OpenThoughts-Agent}},
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howpublished = {https://open-thoughts.ai/agent},
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year = {2025}
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}
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```
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