Qwen3-8B-Base-Agent/README.md

license, base_model, pipeline_tag, library_name, tags

license	base_model	pipeline_tag	library_name	tags
apache-2.0	Qwen/Qwen3-8B	text-generation	transformers	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 · Code · Project page · Collection

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	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}
}