qwen35-9b-openthoughts

Image: image

💻 Code · 🤗 Models & Data · 📜 Paper · 📓 Blog

Note. For full information, go check out the Tmax paper here.

Qwen 3.5 9B - OpenThoughts Agent#

This is a model trained using DPPO on top of Qwen 3.5 9B for use as a terminal-agent. This model was trained as an ablation on the Openthinker-Agent dataset.

This model is part of a collection of terminal agents in various sizes.

Additionally, we provide model checkpoints as branches of the repository. The main model checkpoint is step 300 as this performed best on TBLite.

Evaluation Results#

For details on evaluation methodology please check our paper. In general, we used a podman (docker) backend with default timeouts and custom harness similar to mini-swe-agent.

Model Details#

Model Description#

• Developed by: Ai2
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model: Qwen 3.5 9B
• Dataset: OpenThoughts-Agent

Hyperparameters#

This model was trained using DPPO with the following hyperparameters:

• base model: hamishivi/Qwen3.5-9B
• Max prompt tokens: 2048
• Max per-turn tokens: 16384
• Max overall tokens: 65536
• Pack length: 67584
• Per-device train batch size: 1
• Unique prompts per rollout: 8
• Samples per prompt rollout: 32
• Async steps: 4
• Max steps: 64
• Learning rate: 1e-6
• LR scheduler: constant
• Total training steps: 500 steps (this checkpoint is from 200 steps of training, which performed best on TBLite)
• Sampling Temperature: 1.0
• KL Beta: 0.0
• Loss fn: DPPO
• Divergence: binary TV
• TV threshold: 0.1
• Advantage normalization: centered (no division by stdev)
• FP32 LM head: true

For more details on training, please see our codebase.

License#

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Citation#

If you use our model or data, please cite our paper:

@misc{ivison2026tmaxsimplerecipeterminal,
      title={Tmax: A simple recipe for terminal agents}, 
      author={Hamish Ivison and Junjie Oscar Yin and Rulin Shao and Teng Xiao and Nathan Lambert and Hannaneh Hajishirzi},
      year={2026},
      eprint={2606.23321},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.23321}, 
}