---
license: apache-2.0
base_model: Qwen/Qwen3-8B
pipeline_tag: text-generation
library_name: transformers
tags:
  - agent
  - multi-turn
  - reinforcement-learning
---

# Qwen3-8B-Base-Agent

The Base agent from MIMESIS: Qwen3-8B after a short supervised warm start, before multi-turn reinforcement learning. The RL agents in the collection all start from this checkpoint.

[Paper](https://arxiv.org/abs/2610.09484) · [Code](https://github.com/VietHoang1512/mimesis) · [Project page](https://viethoang1512.github.io/mimesis/) · [Collection](https://huggingface.co/collections/phanviethoang1512/mimesis)

## Results

Mean task score of the five Qwen3-8B agents from the paper across eight UserRL environments, three of them held out from training (IntentionGym, TelepathyGym, SearchGym), under nine evaluation user models that were not used in training.

| Agent | Training user | Objective | Mean score |
|---|---|---|---:|
| **Base (this model)** | none | SFT | 18.49 |
| [UserRL](https://huggingface.co/phanviethoang1512/Qwen3-8B-UserRL-Agent) | GPT-5.5 | GRPO | 26.10 |
| [InfoPO](https://huggingface.co/phanviethoang1512/Qwen3-8B-InfoPO-Agent) | MIMESIS-9B | InfoPO | 26.65 |
| [UserRL+](https://huggingface.co/phanviethoang1512/Qwen3-8B-UserRL-Plus-Agent) | MIMESIS-9B | GRPO | 29.54 |
| [CSD](https://huggingface.co/phanviethoang1512/Qwen3-8B-CSD-Agent) | MIMESIS-9B | GRPO + CSD | **31.09** |

Each score is the mean over the nine evaluation users; per-user and per-environment scores are in Appendix D of the paper.

## Training

Full fine-tuning of Qwen3-8B on about 1,000 multi-turn trajectories from the five training environments (TravelGym, TurtleGym, FunctionGym, TauGym and PersuadeGym). The trajectories were generated with GPT-4o playing both the agent and the user, keeping the top-ranked ones in each environment, and each agent turn is reasoning in `<think>` tags followed by one tool call.

## Usage

The model loads like Qwen3-8B. To evaluate it in the eight environments, install the [agent code](https://github.com/VietHoang1512/mimesis/tree/main/agent), serve a user model as described there, and run from the `agent` directory:

```bash
bash scripts/run_eval.sh phanviethoang1512/Qwen3-8B-Base-Agent base
```

## Citation

```bibtex
@article{phan2026mimesis,
  title   = {{MIMESIS}: Learning User Simulators as Training Environments for Interactive Agents},
  author  = {Phan, Hoang and Huynh, Dat and Zhmoginov, Andrey and Zeng, Qi and Mu, Wancen and Cao, Yue and Bi, Shengjie and He, Yun and Oh, Changdae and Lei, Deren},
  journal = {arXiv preprint arXiv:2610.09484},
  year    = {2026}
}
```