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. For the 'daytona' runs, we used the daytona backend. For Lite/2.1, we show mean and standard error over 3 runs. For daytona, we show it over 5 runs.

Model Details

Model Description

• Developed by: Ai2
• Language(s) (NLP): English
• License: Apache 2.0
• Finetuned from model [optional]: Qwen 3.5 9B
• Dataset: TMax-15k

Use

To use this model, we recommend serving with vllm (or your inference framework of choice) with:

uvx vllm==0.19.1 serve allenai/tmax-9b \
  --served-model-name tmax-9b \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_xml \
  --port 8008 \
  --max-model-len 65536 \
  --tensor-parallel-size 8 \
  --language_model_only

Make sure to set language_model_only as we removed the vision head during training.

For more details on evaluation, please see our codebase.

Hyperparameters

This model was trained using DPPO with the following hyperparameters:

• base model: hamishivi/Qwen3.5-9B
• Dataset: tmax 15K
• 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}, 
}