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allenai / tmax-9b
Model's Last Updated: June 23 2026
Introduction of tmax-9b
Model Details of tmax-9b
💻 Code · 🤗 Models & Data · 📜 Paper · 📓 Blog
For full information, go check out the Tmax paper here .
TMax 9B
TMax 9B is a model trained using DPPO on top of Qwen 3.5 9B for use as a terminal-agent. It achieves roughly 27% on Terminal Bench 2.0 after 200 steps of RL training, improving by ~6 points over Qwen 3.5 9B with our harness.
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 200 as this performed best on TBLite.
We also provide all model rollouts and logprobs over the course of training here ! This allows you to inspect what went on during our runs. See the folder for more details (I recommend pointing an agent at it to make plots, etc).
Evaluation Results
| Model | TB Lite | TB 2.1 | TB 2.0 (daytona) |
|---|---|---|---|
| Qwen 3.5 2B | 5.71 +/- 1.6 | 1.9 +/- 1.4 | 2.3 +/- 1.0 |
| Tmax 2B | 11.8 +/- 1.4 | 4.2 +/- 1.2 | 2.9 +/- 0.6 |
| Qwen 3.5 4B | 31.8 +/- 3.8 | ? | 16.6 +/- 1.7 |
| Tmax 4B | 42.6 +/- 1.5 | 19.9 +/- 1.1 | 18.9 +/- 1.9 |
| Qwen 3.5 9B | 41.9 +/- 2.7 | 16.1 +/- 3.7 | 21.1 +/- 2.6 |
| Tmax 9B (this model!) | 57.2 +/- 2.5 | 28.8 +/- 3.7 | 27.2 +/- 1.5 |
| Qwen 3.6 27B | 70.8 +/- 2.1 | 40.5 +/- 2.4 | 39.6 +/- 2.1 |
| Tmax 27B | 68.6 +/- 4.7 | 44.9 +/- 1.8 | 42.7 +/- 0.7 |
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{ivison2026tmax,
title={{TMAX}: A Simple Recipe for Terminal Agents},
author={Ivison, Hamish and Yin, Junjie Oscar and Shao, Rulin and Xiao, Teng and Lambert, Nathan and Hajishirzi, Hannaneh},
year={2026},
}
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