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.
Table with columns: Agent, Training user, Objective, Mean score| Agent | Training user | Objective | Mean score |
|---|
| Base | none | SFT | 18.49 |
| UserRL | GPT-5.5 | GRPO | 26.10 |
| InfoPO | MIMESIS-9B | InfoPO | 26.65 |
| UserRL+ (this model) | MIMESIS-9B | GRPO | 29.54 |
| CSD | MIMESIS-9B | GRPO + CSD | 31.09 |
With the GRPO objective fixed, replacing GPT-5.5 with MIMESIS-9B as the training user improves the mean score under every evaluation user, and adding CSD improves it further under all nine. Each score is the mean over the nine evaluation users; per-user and per-environment scores are in Appendix D of the paper.
Training
RL starts from the Base checkpoint, Qwen3-8B after a supervised warm start, and trains on five UserRL environments: TravelGym, TurtleGym, FunctionGym, TauGym and PersuadeGym. The full configuration is in Appendix D of the paper, and the training scripts are in the agent directory of the code release.
Usage
The model loads like Qwen3-8B. To evaluate it in the eight environments, install the agent code, serve a user model as described there, and run from the agent directory:
bash scripts/run_eval.sh phanviethoang1512/Qwen3-8B-UserRL-Plus-Agent userrl-plus
Citation
@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}}