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 (this model) | none | SFT | 18.49 |
| UserRL | GPT-5.5 | GRPO | 26.10 |
| InfoPO | MIMESIS-9B | InfoPO | 26.65 |
| UserRL+ | MIMESIS-9B | GRPO | 29.54 |
| CSD | 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, serve a user model as described there, and run from the agent directory:
bash scripts/run_eval.sh phanviethoang1512/Qwen3-8B-Base-Agent base
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}}